Abstract

Accurate image reconstruction in color lens-free imaging has proven challenging. The color image reconstruction of a sample is impacted not only by how strongly the illumination intensity is absorbed at a given spectral range, but also by the lack of phase information recorded on the image sensor. We present a compact and cost-effective approach of addressing the need for phase retrieval to enable robust color image reconstruction in lens-free imaging. The amplitude images obtained at transparent wavelength bands are used to estimate the phase in highly absorbed wavelength bands. The accurate phase information, obtained through our iterative algorithm, removes the color artefacts due to twin-image noise in the reconstructed image and improves image reconstruction quality to allow accurate color reconstruction. This could enable the technique to be applied for imaging of stained pathology slides, an important tool in medical diagnostics.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

In recent years, lens-free imaging (LFI) has evolved into a widely applied imaging technique, based on the principles of in-line holography [1,2]. Its lensless design offers the advantage of a compact and cost-effective imaging system compared to conventional microscopes [3,4]. Lens-free imaging systems have been demonstrated for both industrial and life-sciences applications, including metrology applications, imaging of live cell cultures, analysis of cardiac cell contractility and blood screening with flow cytometry [413]. The absence of objective lenses and other opto-mechanical components enables imaging over full field-of-view, only limited by the image sensor size. The imaged object is positioned directly between light source and image sensor as illustrated in Fig. 1(a). The light field transmitted through the object is captured using a CMOS image sensor and numerically reconstructed to an image in-focus from the captured hologram. In theory, the hologram contains both information on the amplitude (transmittance) of the object, as well as the phase delay induced on the transmitted light field. CMOS image sensors are only sensitive to intensity of the incoming light and hence, information on the phase delay is lost at the image sensor during hologram acquisition. This results in the appearance of the object’s twin image after image reconstruction [1416]. To solve this issue various phase retrieval algorithms have been developed [1624]. Retrieving the lost phase information is crucial to significantly improve lens-free image quality and resolution [1416,21,24]. These phase retrieval techniques have focused mostly on gray-scale images. More recently, important improvements in lens-free image reconstruction were obtained through the introduction of several deep-learning based techniques to perform phase retrieval, improve image resolution or use a deep-learning network to match lens-free image reconstructions with brightfield microscope images [2528]. Such approach requires careful training of the network but once trained, a deep-learning network performs its task accurately, and efficiently.

 

Fig. 1. Conventional color lens-free imaging approach using illumination at primary RGB wavelengths. (a) Typical configuration for in-line (color) lens-free imaging. The light source illuminates the sample sequentially with blue, green and red light. The resulting scattered object field and unscattered reference field propagate to the image sensor. (b) The interference pattern (hologram) is detected at the image sensor for each illumination wavelength. (c) Backwards propagation of the hologram results in a numerically reconstructed image of the object. (d) Combination of amplitude images obtained at blue, green and red wavelengths, gives a color image reconstruction of the object. The object in this image is a cross section of a lily anther.

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Color digital holography could enable a whole new range of applications, including water quality monitoring [29], flow analysis [30,31], metrology [3234] and microbiology applications [35]. Imaging of stained pathology slides relies on reconstruction of the tissue’s color. The combination of tissue type and applied stain encodes additional information and contrast for the pathologist to use during sample analysis. This technique is an important tool in medical diagnostics and requires accurate reconstruction of the sample’s color to differentiate between healthy and affected tissue [36,37]. The conventional method for color lens-free imaging (CLFI) is to acquire holograms at wavelengths corresponding to the three primary colors (blue, green, red) and then reconstruct the captured holograms at the respective wavelength [38]. By combining the amplitude images into the red, green and blue channel of an RGB image, a reconstructed color image of the object is obtained. Figure 1 demonstrates this conventional CLFI approach. Recently, several approaches for lens-free color reconstruction have been reported [39,40]. Using a color image sensor with Bayer color filter [41], sequential or simultaneous illumination can be used at primary wavelengths. Due to the filter array’s selective sensitivity for different wavelengths, this technique results in only partial holograms and demosaicing of the captured holograms is required, but still results in low resolution holograms. As a solution, pixel super-resolution (PSR) techniques are used. Using PSR, a higher resolution hologram, can be obtained at each wavelength from multiple low-resolution holograms by lateral shifting of the sample or sensor [26,42,43]. When combined with a multi-height based phase retrieval, accurate color images can be reconstructed either through spectral estimation or using an alternative deep-learning approach [44,45]. This technique requires mechanical scanning of the sample or sensor in three axes and capturing several holograms at each illumination wavelength. As a result, it is a time consuming and data intensive approach. Alternatively, phase recovery can also be done using a deep-learning approach, reducing the imaging system’s complexity, but introducing a learning step in the process [25,28]. An important challenge arises when the sample is densely stained and strongly absorbs the light intensity at a given wavelength. This results in low signal-to-noise ratio (SNR) in the captured hologram. The low-SNR amplitude information, hinders correct interpretation of the phase retrieval convergence criteria, leading to noise corrupted phase information [17]. Twin-image related artefacts will result in poor reconstruction quality and inaccurate color reconstruction. In this paper, we demonstrate a multi-wavelength illumination scheme, allowing robust image reconstruction in color lens-free imaging. This is achieved through estimating accurate phase images at the corresponding illumination bands even if the hologram suffers from low SNR due to strong light absorption at a given wavelength. An iterative phase retrieval technique is developed to estimate the phase from high-SNR holograms and map it to different spectral bands. Thus, twin-image suppression, achieved through accurate phase estimation at transparent illumination bands, leads to improved image and color reconstruction. As our approach does not rely on mechanical scanning, it is fast, reliable and cost-effective compared to existing CLFI techniques. This provides a robust and compact solution for CLFI to be used for color imaging applications, such as imaging of stained pathology slides.

2. Theory and methods

2.1 Simulating color lens-free imaging

In in-line holography, the object is placed directly between the light source and the image sensor as in Fig. 1(a). When the illuminating light field reaches the object, it is partially transmitted through the object and partially interacts with the object. This results in a reference wave, $R(x,y)$ and a scattered object wave, $O(x,y)$, respectively. The spatial coordinates $(x,y)$ are defined in the object plane. The reference and object wave propagate to the image sensor, where the resulting interference pattern (hologram) intensity is captured. In general, the object can be described by its transmission function, $t(x,y)$ [46,47]:

$$O(x,y,\lambda_i) = t(x,y,\lambda_i) = A(x,y;\lambda_i)\textrm{exp}[j\varphi(x,y;\lambda_i)]$$
In this equation, $A$ and $\varphi$ model the transmittance and phase shifting profile of the object as a function of free-space illumination wavelength $\lambda _i$. The recorded hologram intensity is the result of the convolution of reference and object wave with a propagation kernel in the Fourier domain. For hologram generation and reconstruction in in-line holography, the angular spectrum propagation is applied and is given by [46,4850]:
$$H_i(f_x, f_y,z;\lambda_i) = \textrm{exp}\bigg(\dfrac{2\pi jz}{\lambda_i}\sqrt{1-(\lambda_i f_x)^{2}-(\lambda_i f_y)^{2}}\bigg)$$
In Eq. (2), $(f_x, f_y)$ are spatial frequency coordinates and $z$ is the distance between sample and sensor, the reconstruction distance. Once a hologram has been acquired, field propagation in the opposite direction of wave propagation is performed over negative distance $z$.
$$\begin{aligned} O(x,y,z;\lambda_i) &= \mathcal{F}^{-1}\big[\mathcal{F}(\sqrt{I_{\textrm{det}}(X,Y,0;\lambda_i)}\times H_i^{*}(f_x,f_y,z;\lambda_i)\big] \\ &= A(x,y,z;\lambda_i)\textrm{exp}[j\varphi(x,y,z;\lambda_i)] \end{aligned}$$
In Eq. (3), $\mathcal {F}$ is the Fourier transform, $I_{\textrm {det}}(X,Y,0;\lambda _i)$ is the detected hologram intensity with $(X,Y)$ spatial coordinates in the detector plane at $z=0$. Finally, $H^{*}$ indicates the complex conjugate of the propagation kernel given in Eq. (2) to propagate the acquired hologram from image sensor back to object plane at $z=z$. The solution of Eq. (3) gives the reconstructed transmittance, $A(x,y,z;\lambda _i)$, and a phase profile, $\varphi (x,y,z;\lambda _i)$, of the object in a plane at distance $z$ from the image sensor. To illustrate the concept of color lens-free imaging, a simple synthetic object with constituents of varying size, shape and spectral properties is simulated using this theoretical model. Figure 2(a) shows the RGB image representation of this synthetic object in which three channels are linked to the transmittance of the object at corresponding illumination wavelengths. These transmittance profiles are shown in Fig. 2(b). In these simulations, no phase shifting properties are introduced in the object constituents. Holograms of the object are generated at the following three primary wavelengths: $\lambda _{\textrm {B}}=450~nm$, $\lambda _{\textrm {G}}=520~nm$ and $\lambda _{\textrm {R}}=630~nm$ and are shown in Fig. 2(c). The different intensities in the acquired holograms reveal different levels of transmittance through the object. After the reconstruction process as explained in Eq. (1)–(3), an estimation of the object transmittance at each illumination wavelength is obtained (Fig. 2(d)). Through combining the estimated transmittance at three wavelengths, a color image representation of the object is obtained (Fig. 2(e)). In Fig. 2(f), the transmittance profiles at the cross sections indicated with dotted lines in Fig. 2(b) are shown. The simulated object is synthetically constructed to demonstrate how the wavelength dependence of the object’s optical properties propagates in the lens-free imaging flow, with specific interest in color formation from object transmittance. An important observation is that wavelength-dependent fringes in the amplitude images, result in color artefacts around the object in the color reconstruction due to the lack of phase information the recorded holograms. This indicates the need for phase retrieval to achieve accurate color lens-free imaging.

 

Fig. 2. Color LFI simulations. (a) RGB image representation of the synthetic object in which three channels are linked to the transmittance of the object at a corresponding illumination wavelength. (b) Defined transmittance profile at the illumination wavelengths. (c) Captured hologram intensities at each illumination wavelength. (d) Estimated transmittance profiles after reconstruction. (e) Color image representation through combination of the transmittance profiles from (d). (f) Transmittance profile obtained from the cross-sections indicated in (b). Illumination wavelengths 450nm, 520nm and 630nm are indicated through the blue, green and red outline in (b)-(d).

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2.2 Phase retrieval for color LFI

Mitigating the twin image through phase retrieval is critical for color lens-free imaging. Former studies on CLFI used multi-height phase retrieval that relies on using multiple images obtained at different sample to sensor distances per illumination band [44]. Despite its effectiveness, such approach limits the practicality and compactness of the imaging system. Alternatively, deep-learning based CLFI can be considered to image colored samples [28,45]. In this work, we adopt a multi-wavelength based phase retrieval approach due to its simplicity and also compatibility with CLFI concept. We investigate exploiting the wavelength diversity, not only for color reconstruction, but also for obtaining accurate phase images of the objects. First, multi-wavelength phase retrieval is introduced in section 2.2.1. Next, we present a new phase retrieval technique for color lens-free imaging in section 2.2.2, which is based on the multi-wavelength phase retrieval in transparent wavelength bands.

2.2.1 Multi-wavelength phase retrieval

As opposed to multi-height phase retrieval techniques, the multi-wavelength approach relies on capturing images of the same object at (slightly) differing illumination wavelengths. While the reconstruction distance for the hologram acquired at a given wavelength is fixed, holograms acquired at other wavelengths can be reconstructed as if they were in a plane at a relatively larger or smaller reconstruction depth. This allows to iterate between the captured holograms in order to retrieve the phase via virtual z-stepping [2224]. The relative depths of the hologram planes are indicated in Fig. 3(a) (left) and each iteration of the multi-wavelength phase retrieval method propagates between these planes as indicated by steps ① - ④. The flow in Fig. 3(b) illustrates a single iteration of the multi-wavelength phase retrieval. Mathematically, each iteration consists of the following steps. An estimation for the object’s phase profile $\varphi _1$ is combined with the hologram amplitude $A_1$, captured at wavelength $\lambda _1$. This results in a complex hologram that is propagated to the relative depth of wavelength $\lambda _2$. The propagation kernel from Eq. (2) is used, with the difference in reconstruction depth as argument for $z$. This results in a calculated hologram as if it were acquired at wavelength $\lambda _2$. The calculated hologram amplitude, $A'_2$ is replaced by the acquired amplitude $A_2$, while phase is retained. Steps ② - ④ follow the same pattern propagating between the different planes:

oe-28-22-33002-e001

 

Fig. 3. Multi-wavelength phase retrieval process. (a) Relative depths of the hologram planes. (b) Multi-wavelength phase retrieved amplitude images are placed in the RGB channel of a color reconstructed image. Mixing of the object distribution at different illumination wavelengths, results in erroneous image reconstructions. (c) Detailed flow for one iteration of the multi-wavelength phase retrieval. Complex holograms, (amplitude and phase) are propagated between the different relative hologram planes. At each plane, calculated amplitude, $A_i'$ is replaced with the amplitude captured by the image sensor, $A_i$, while phase is retained as is indicated by the color-code and arrows.

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In Eq. (4), ’$\rightarrow$’ indicates the reconstruction according to Eq. (3). $A'_i$ and $A_i$ are the calculated and acquired hologram amplitude for wavelength $\lambda _i$. After one iteration, an estimation of the object’s phase profile for wavelength $\lambda _1$ is obtained. This estimation of the phase profile is combined with the acquired hologram amplitude. Once convergence criteria are met or after a certain number of predefined iterations, a final reconstruction step (Fig. 3(a) step ⑤) results in a phase retrieved object image for wavelength $\lambda _1$. We evaluated how multi-wavelength phase retrieval would behave when imaging colored samples and using RGB primary wavelengths in the light source. It enables to assess the validity of this phase retrieval technique for CLFI. For this evaluation, the method described in this section is repeated, using each primary wavelength as a starting point for the multi-wavelength phase retrieval algorithm. The resulting amplitude images in Fig. 3(b) are combined in the blue, green and red channel of a color image (right). It is important to note that the different hologram amplitudes, acquired at each primary wavelength, are used throughout phase retrieval. This implies that the wavelength-dependent absorption properties of the object, will determine the outcome of the phase retrieval procedure. If the object strongly absorbs one of the illumination wavelength intensities, very little signal arrives at the image sensor and accordingly, the recorded hologram exhibits low SNR. These holograms are important for color reconstruction since they encode the color information, as observed in Fig. 2. However, the wavelength-dependent transmission properties don’t satisfy the assumption that optical properties of the imaged tissue are uniform across the wavelength spectrum used for phase retrieval [22,51]. Hence, applying this technique for color lens-free imaging of real samples will result in erroneous image reconstructions as is demonstrated in section 3.1 and discussed in section 3.2.4.

2.2.2 High-SNR phase retrieval

To overcome the aforementioned issues, we demonstrate a new phase retrieval technique for CLFI using only high-SNR holograms, acquired at a narrow transparent wavelength band. Noise corrupted phase and amplitude reconstructions are avoided by excluding low-SNR holograms from phase retrieval. The retrieved phase profile is then mapped to other illumination wavelengths and combined with captured hologram amplitude for image reconstruction. With improved estimation of the object’s phase profile, structural details of the imaged object can be recovered, even in case of low-SNR holograms. This technique requires at least two high-SNR holograms, acquired in a narrow spectral band, that are submitted to a similar multi-wavelength phase retrieval procedure as was shown in Fig. 3(b). Once the phase profile of the object is retrieved, the second step of the high-SNR image processing, illustrated in Fig. 4, can be initiated. The retrieved phase profile is recalculated and combined with amplitude information, captured at other wavelengths. For this technique, the phase delay induced by the sample is assumed to depend on wavelength as follows according to [24]:

$$\Delta\varphi_i = \dfrac{2\pi\Delta t}{\lambda_i}(n_\textrm{obj}-n_\textrm{med})$$
In Eq. (5), $\Delta t$ is the physical thickness of the sample, $n_{\textrm {obj}}$ and $n_{\textrm {med}}$ are the refractive index of the object and the surrounding medium respectively. $\lambda _i$ is the free space wavelength for which the phase information is calculated. The goal is to calculate the phase profile as if it was illuminated using another wavelength, without the wavelength-dependent absorption interfering with phase-retrieval performance. We therefore first retrieve the phase profile using holograms captured in a transparent wavelength band and map it to other illumination wavelengths. We treat the sample as if the refractive index contrast to the object’s surrounding medium is induced by the object only and not due to the applied stain. This allows to approximate the object’s phase profile at other illumination wavelengths as follows [47,52]:
$$\Delta \varphi_j \approx \dfrac{\lambda_i}{\lambda_j}\Delta \varphi_i$$
In Eq. (6), $\Delta \varphi _j$ is the phase delay for wavelength $\lambda _j$ calculated starting from the phase delay $\Delta \varphi _i$ retrieved for $\lambda _i$. The flowchart in Fig. 4 illustrates the full image processing procedure. After high-SNR phase retrieval, the obtained phase profile is converted for each wavelength in the light source. After propagation to the hologram plane, the calculated amplitude is replaced by the acquired hologram amplitude. Once, the hologram amplitude is combined with the optimized estimation for the phase profile, reconstruction to the object plane results in improved image reconstruction quality. Applying this method to each primary wavelength and combination of the amplitude images in the red, green and blue channel a color image, results in a high-quality CLFI image.

 

Fig. 4. High-SNR image reconstruction for color lens-free imaging. Starting from amplitude and phase information, retrieved using high-SNR holograms, a complex hologram is calculated. The calculated hologram amplitude is replaced with the detected hologram amplitude at that wavelength. A final image reconstruction results in improved image reconstruction quality and robust reconstruction of the object’s transmission properties due to adequate phase information in the hologram plane.

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2.3 Hologram acquisition

The experimental setup is schematically illustrated in Fig. 5. The light source consists of four pig-tailed laser diodes, coupled to an RGB-coupler (Thorlabs RGB26) with a single output fiber. The wavelengths in the light source are: $\lambda _{\textrm {B}}=450~nm$, $\lambda _{\textrm {G}}=520~nm$, $\lambda _{\textrm {R1}}=640~nm$, $\lambda _{\textrm {R2}}=660~nm$. The choice for these wavelengths is motivated based on two criteria. First, the electronic hardware allows to drive four light sources separately, hence limiting the number of laser diodes to max. four. Second, the samples in our study are mostly transparent in the red wavelength range thus we chose to perform phase retrieval using two light sources in this spectral range. The image sensor (TOSHIBA TC358743XBG, 13-megapixel, monochrome sensor, $1.12~\mu m$ pixel size) sequentially captures full resolution holograms holograms at each wavelength.

 

Fig. 5. Schematic drawing of experimental setup: 1) Four pig-tailed laser diodes coupled to an RGB coupler 2), resulting in single output fiber 3). A colored object 4) is placed directly between light source and image sensor 5).

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3. Experimental results and discussion

3.1 Experimental results

3.1.1 Lens-free imaging of colored objects

First, we compare the performance of our high-SNR phase retrieval technique with RGB multi-wavelength phase retrieval for mostly transparent samples, where only small parts absorb the illumination light. In these regions, low-SNR in the captured hologram impedes accurate image reconstruction in two ways. The first effect is loss of structural details in the image reconstructions. Secondly, with increasing number of iterations of RGB multi-wavelength phase retrieval, the low-SNR regions distort the amplitude image reconstructions due to mixing of the wavelength-dependent sample distribution. When combining the amplitude images to color image reconstructions, these two effects result in loss of image detail and color distortion. The image reconstruction results are summarized in Fig. 6. For this analysis, a color stained cross section of a lily anther is used. The result from the conventional CLFI method, without phase retrieval, is shown in Fig. 6(a). The lack of phase retrieval results in low-resolution color images. The RGB phase retrieval method efficiently reduces the amount of twin image, but structural details are lost, as indicated by the blue arrows. Our high-SNR technique, while reducing twin-image related color artefacts, successfully reconstructs the structural details in the color image, as can be seen in Fig. 6(c). As a reference, a brightfield microscope image (Leica DM5000B microscope with Thorlabs DCC1645C camera and 4x - 0.13NA objective) is shown in Fig. 6(d). To compare the performance of the high-SNR technique, an analysis based on the structural similarity index metric (SSIM) was done [53]. When comparing the phase retrieved color images from Fig. 6(b) and (c) in the region of interest (ROI) indicated by the blue arrows, an improvement of $1.7\%$ is obtained using our high-SNR method due to successful reconstruction of the structural details. A more detailed analysis is provided in the discussion in section 3.2.2.

 

Fig. 6. Comparison of three color lens-free imaging approaches. (a) Conventional combination of reconstructed holograms. (b) Color lens-free imaging using RGB holograms in the phase retrieval procedure. (c) Color lens-free imaging using high-SNR reconstruction. (d) Bright-field microscope reference images.

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3.1.2 Color lens-free imaging of densely stained pathology samples

In the case of densely stained pathology samples, the effects of low-SNR holograms will impact the color image reconstruction more severely. Performance of our high-SNR phase retrieval technique is compared with the conventional CLFI and RGB multi-wavelength phase retrieval method. Again, we emphasize that direct application of multi-wavelength phase retrieval to CLFI results in erroneous image reconstructions due to mixing of wavelength-dependent sample distributions. For this comparison, Bielschowsky stained brain tissue is used and the results are summarized in Fig. 7. As can be seen from Fig. 7(a) both blue and green illuminations are strongly absorbed, resulting in low-SNR holograms. The conventional CLFI method shows color artefacts due to wavelength-dependent twin-image and lack of structural details due to low-SNR holograms. In Fig. 7(b)-(c), the comparison of RGB multi-wavelength phase retrieval with the high-SNR phase retrieval technique is shown. The color image obtained with high-SNR reconstruction technique shows higher amount of details while preserving the color information from the applied stain. This claim is supported by an improvement of $1.8\%$ in SSIM when using the high-SNR phase retrieval. In this experiment, the holograms captured at 640 nm and 660 nm were used to obtain the initial phase estimation in the high-SNR method. The phase profile was obtained after 10 iterations of high-SNR phase retrieval. After combination with captured amplitude holograms, the amplitude image reconstructions from Fig. 7(d) were obtained. In the RGB approach, noise corrupted phase retrieval results in loss of structural details in the image reconstruction and leads to color distortion. The magnified images demonstrate improved reconstruction performance and color robustness of the high-SNR technique. As a reference, microscope (Leica DM5000B microscope with Thorlabs DCC1645C camera and 4x - 0.13NA objective) images are shown. To confirm these results, several other samples were imaged and processed in a similar way. The results are summarized in Fig. 8. The advantage of using our phase retrieval technique is twofold. First, the structural details are accurately reconstructed as the retrieved phase complements the low-SNR amplitude information in the hologram. Second, the effect of low-SNR holograms on the color accuracy is minimized. The captured hologram amplitude is combined with an accurate phase profile, resulting in high-quality amplitude reconstructions. Absorption properties of the object can hence accurately be reconstructed at the available illumination wavelengths. The use of different tissue - stain combinations indicates broader applicability of the technique for imaging of pathology slides. Since the sample-stain combination is usually known a priori, the additional illumination wavelength channel can be selected according to the stain properties of the sample.

 

Fig. 7. Comparison of color LFI imaging techniques on Bielschowsky stained brain tissue. (a) Captured holograms. (b) Conventional CLFI approach showing twin-image color artefacts. (c) RGB multi-wavelength phase retrieval, indicating color distortion and reduced amount of image details. (d) High-SNR imaging technique showing increased amount of image details and accurate amplitude image reconstructions. (e) Brightfield microscope reference image.

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Fig. 8. Comparison of RGB multi-wavelength phase retrieval and high-SNR imaging technique on imaging of several sample - stain combinations for imaging of pathology slides. Samples include: H&E stained liver tissue, trichrome stained bone tissue, H&E stained appendix tissue and Bielschowsky silver stained brain tissue. Reference images obtained with brightfield microscope are included.

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3.2 Discussion

3.2.1 Color reconstruction from discrete wavelengths

For imaging of pathology slides, clinical diagnosis is directly derived from the color content of the microscope image. It is therefore crucial that colors are reconstructed accurately and reliably. Color management and standardization between different imaging setups is, however, nontrivial as each imaging system uses a specific light source, image sensor and image processing software [54,55]. Here, we discuss color reconstruction in LFI, how to interpret the results and compare with conventional microscope images.

Holograms are acquired using narrow-band light sources such as lasers or LEDs. After numerical reconstruction, a monochrome reconstructed image can be obtained for each wavelength, representing the object’s transmission profile at that respective wavelength. The more wavelengths, the more complete knowledge of the object’s transmission profile available. However, to accommodate lens-free imaging system simplicity, only a few narrow-band laser diodes are used, hence limiting the available spectral information. In general, color image intensity is the result of interaction between illuminant and object, as well as spectral sensitivity of the image sensor. This relationship is given by [38]:

$$I(x,y) = \int S(\lambda)T(x,y,\lambda)R(\lambda)d\lambda$$
In this equation, $S$ is the power spectral density of the illuminant, $T$, the spectral transmittance of the object and $R$, the spectral response of the image sensor at wavelength $\lambda$. This latter is typically given by the Bayer filter array’s response curves for color image sensors, or specified by the manufacturer for monochrome image sensors. When using laser diodes, as in lens-free imaging, the spectrum of the illuminant is centered around a specific central wavelength $\lambda _0$, and Eq. (7) can be approximated as [38]:
$$I(x,y) = S_0T(x,y,\lambda_0)R(\lambda_0)$$
This allows to extract the transmission properties $T(x,y)$, independent of sensor and illuminant intensity, from the detected intensity, by normalizing it with an image taken with no object in the field-of-view. CLFI aims to establish color based on image reconstruction at least at three primary wavelengths. Of course, the technique’s color accuracy is still limited due to the use of a defined amount of narrow-band light sources. This can be addressed by increasing the number of wavelengths, estimating the sample’s spectral properties or using deep-learning based methods [27,38,44,45].

3.2.2 Performance analysis of high-SNR phase retrieval

To evaluate the performance of our high-SNR phase retrieval technique, an analysis based on SSIM was done [53]. Image reconstruction using the high-SNR phase retrieval and RGB multi-wavelength phase retrieval are compared with the reference microscope images shown in Fig. 6 and 8. In order to minimize image aberrations between the lens-free reconstructions and the reference images, image registration technique was applied in ImageJ [56]. No other image processing was applied to the LFI images before comparison with the brightfield reference images, which might lead to lower initial SSIM. However, even a small increase in SSIM can hence indicate a significant improvement in image reconstruction as can be qualitatively observed in Figs. 68.

Table 1 summarizes the results. After comparison of the ROI indicated by the blue arrows in Fig. 6, the SSIM analysis on the image reconstruction, acquired at the blue illumination wavelength, shows similar results, with a $0.3\%$ decrease for our high-SNR method. This can be attributed to the low signal level in the respective hologram. When analyzing the green and red image channels, an improvement of respectively $1.3\%$ and $4.1\%$ is obtained. These results are shown as ’Lily ROI’ in Table 1. Since two different imaging systems are compared, absolute SSIM values are less insightful. What is important is the difference in SSIM between the two phase retrieval techniques, as it reveals the increase or decrease in structural details resolved in the LFI images. In general, our technique demonstrates an improvement in reconstruction of the structural details. Due to the available phase profile, even at low-SNR channels, a high-quality image reconstruction can be obtained which in turn can enable accurate color lens-free imaging.

Tables Icon

Table 1. SSIM comparison of high-SNR phase retrieval with microscope image compared to RGB multi-wavelength phase retrieval.

3.2.3 Multi-wavelength phase retrieval

It is critical to evaluate the effect of the number of intensity images required to perform accurate phase retrieval. Therefore, this section provides a comparison for the performance of multi-wavelength phase retrieval when using two or three wavelengths. For this analysis, a slightly modified experimental setup was used. The wavelengths were $\lambda _1~=$ 640 nm, $\lambda _2~=$ 660 nm and $\lambda _3~=$ 685 nm and a USAF 1951 test target was used. The performance of the phase retrieval, depending on the number of wavelengths used, is evaluated. For two-wavelength phase retrieval, $\lambda _1$ and $\lambda _2$ were used. This choice is based on the presence of these wavelengths in the experimental setup for high-SNR color reconstruction. Alternatively, one could argue to use a combination of the wavelengths available in the CLFI setup from Fig. 5 for the three-wavelength phase retrieval. However, as we are interested to see how phase retrieval performs within a relatively narrow range of the spectrum, to ensure approximately similar optical properties of the sample, we chose to select a third wavelength in the red range. As can be seen in Fig. 9, each method is able to reconstruct USAF group 7 element 6 ($2.2\mu m$), but contrast improves from approximately $48\%$ to $91\%$ when using phase retrieval. The difference between using two or three wavelengths, is negligible. Secondly, a comparison is made based on twin image removal performance. Twin image is quantified by the presence of fringes around the square feature indicated using the red rectangle. For comparison of twin removal, a set of cross sections is averaged as indicated by the red lines in Fig. 9(e). The graphs in Fig. 9(g)-(h) show the result of applying 10 iterations of 2L and 3L phase retrieval compared to the direct reconstruction from Fig. 9(f). The graph in Fig. 9(i) indicates the amount of fringes that were removed with increasing number of iterations of phase retrieval. The results indicate that 2L phase retrieval performs comparable to 3L phase retrieval, both in terms of twin removal rate (removed fringes per increased amount of iterations) and convergence point (best achievable performance). These results motivate the choice for 10 iterations of high-SNR phase retrieval for color reconstruction from section 3.1.2.

 

Fig. 9. Comparison of multi-wavelength phase retrieval with two or three wavelengths. In yellow (a), the achieved contrast of USAF group 7 element 6 increased from $\sim 48\%$ to $\sim 91\%$ when comparing direct reconstruction (b) with multi-wavelength image reconstructions with two (c) and three (d) wavelengths. In (e)-(i), twin image removal is compared between reconstruction without phase retrieval (f), two-wavelength (g) and three-wavelength (h) phase retrieval. The insets in (f)-(h) show the reconstruction result. Graph (i) shows quantification of removal of twin image with increasing number of iterations between 2L and 3L phase retrieval.

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3.2.4 Multi-wavelength phase retrieval for CLFI

Multi-wavelength phase retrieval in principle assumes that the optical index of the sample under investigation is not strongly dispersive across the spectral range of the phase retrieving process. Although most biological objects satisfy this requirement in the visible spectrum, the staining process can alter the refractive index of the intracellular structures due to the Kramers-Kronig relationship [52]. This condition may lead to imprecise phase estimation for strongly absorbing stains unless it is taken into account in the phase retrieval algorithm. The obtained color image can assist in estimating the index changes induced by the stains such that the phase retrieval algorithm can provide a more accurate estimation through refining Eq. (5) and Eq. (6) with index dispersion terms [51]. Phase retrieval in transparent wavelength bands to complement low-SNR amplitude information in strongly absorbed wavelength bands resulted in high-quality image reconstructions with accurate color. Including the effect of the applied stain in the refractive index calculations presents a possible refinement of the demonstrated technique.

4. Conclusion

We demonstrate a technique for robust image reconstruction in color lens free imaging. We rely on performing a multi-wavelength phase retrieval in a spectral band in which the object is highly transparent and thus high-SNR holograms can be obtained. Then the calculated phase is used to compensate missing phase information in the color forming holograms. Our method eliminates the need for time-consuming mechanical or wavelength-based scanning of the sample. Requiring only holograms acquired at a selection of wavelengths and using optimized phase estimation, our method accurately reproduces the color in the sample at the illumination wavelengths and gives well-resolved image reconstructions. This technique provides a robust framework for accurate lens-free imaging of pathology tissue slides in medical diagnostics.

Funding

Fonds Wetenschappelijk Onderzoek (1S66718N).

Acknowledgments

J. Mariën acknowledges Z. Luo for all fruitful discussions, shared experience and insights, G. Vanmeerbeeck for his help with Python programming and Z. Lin for help with the design and assembly of the experimental setup.

Disclosures

The authors declare no conflicts of interest.

References

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3. S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012). [CrossRef]  

4. O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010). [CrossRef]  

5. G. Coppola, P. Ferraro, M. Iodice, S. De Nicola, A. Finizio, and S. Grilli, “A digital holographic microscope for complete characterization of microelectromechanical systems,” Meas. Sci. Technol. 15(3), 529–539 (2004). [CrossRef]  

6. S. V. Kesavan, F. P. N. Y. Garcia, M. Menneteau, F. Mittler, B. David-Watine, N. Dubrulle, S. L. Shorte, B. Chalmond, J.-M. Dinten, and C. P. Allier, “Real-time label-free detection of dividing cells by means of lensfree video-microscopy,” J. Biomed. Opt. 19(3), 1 (2014). [CrossRef]  

7. S. V. Kesavan, F. Momey, O. Cioni, B. David-Watine, N. Dubrulle, S. Shorte, E. Sulpice, D. Freida, B. Chalmond, J. Dinten, X. Gidrol, and C. Allier, “High-throughput monitoring of major cell functions by means of lensfree video microscopy,” Sci. Rep. 4(1), 5942 (2015). [CrossRef]  

8. Y. Sung, N. Lue, B. Hamza, J. Martel, D. Irimia, R. R. Dasari, W. Choi, Z. Yaqoob, and P. So, “Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel,” Phys. Rev. Appl. 1(1), 014002 (2014). [CrossRef]  

9. D. Vercruysse, A. Dusa, R. Stahl, G. Vanmeerbeeck, K. de Wijs, C. Liu, D. Prodanov, P. Peumans, and L. Lagae, “Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer,” Lab Chip 15(4), 1123–1132 (2015). [CrossRef]  

10. E. Mathieu, C. D. Paul, R. Stahl, G. Vanmeerbeeck, V. Reumers, C. Liu, K. Konstantopoulos, and L. Lagae, “Time-lapse lens-free imaging of cell migration in diverse physical microenvironments,” Lab Chip 16(17), 3304–3316 (2016). [CrossRef]  

11. C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017). [CrossRef]  

12. T. Pauwelyn, R. Stahl, L. Mayo, X. Zheng, A. Lambrechts, S. Janssens, L. Lagae, V. Reumers, and D. Braeken, “Reflective lens-free imaging on high-density silicon microelectrode arrays for monitoring and evaluation of in vitro cardiac contractility,” Biomed. Opt. Express 9(4), 1827–1841 (2018). [CrossRef]  

13. Y. Fang, N. Yu, Y. Jiang, and C. Dang, “High-precision lens-less flow cytometer on a chip,” Micromachines 9(5), 227 (2018). [CrossRef]  

14. T. Latychevskaia and H.-W. Fink, “Solution to the twin image problem in holography,” Phys. Rev. Lett. 98(23), 233901 (2007). [CrossRef]  

15. B. M. Hennelly, D. P. Kelly, N. Pandey, and D. S. Monaghan, “Review of twin reduction and twin removal techniques in holography,” in Proceedings of CIICT 2009, (National University of Ireland Maynooth, 2009), pp. 241–245.

16. L. Denis, C. Fournier, T. Fournel, and C. Ducottet, “Twin-image noise reduction by phase retrieval in in-line digital holography,” Proc. SPIE 5914, 59140J (2005). [CrossRef]  

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22. R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014). [CrossRef]  

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27. Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. De Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3d imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25–27 (2019). [CrossRef]  

28. Y. Rivenson, Y. Wu, and A. Ozcan, “Deep learning in holography and coherent imaging,” Light: Sci. Appl. 8(1), 85–88 (2019). [CrossRef]  

29. Z. Göröcs, L. Orzó, M. Kiss, V. Tóth, and S. Tökés, “In-line color digital holographic microscope for water quality measurements,” Proc. SPIE 7376, 737614 (2010). [CrossRef]  

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36. E. L. Clarke and D. Treanor, “Colour in digital pathology: a review,” Histopathology 70(2), 153–163 (2017). [CrossRef]  

37. P. Shrestha and B. Hulsken, “Color accuracy and reproducibility in whole slide imaging scanners,” J. Med. Imag. 1(2), 027501 (2014). [CrossRef]  

38. P. Xia, Y. Ito, Y. Shimozato, T. Tahara, T. Kakue, Y. Awatsuji, K. Nishio, S. Ura, T. Kubota, and O. Matoba, “Digital holography using spectral estimation technique,” J. Disp. Technol. 10(3), 235–242 (2014).

39. S. O. Isikman, I. Sencan, O. Mudanyali, W. Bishara, C. Oztoprak, and A. Ozcan, “Color and monochrome lensless on-chip imaging of caenorhabditis elegans over a wide field-of-view,” Lab Chip 10(9), 1109–1112 (2010). [CrossRef]  

40. C. Allier, S. V. Kesavan, Y. Hennequin, O. Cioni, F. Momey, T. Bordy, L. Herve, J.-G. Coutard, S. Morel, A. Berdeu, F. Navarro, and J.-M. Dinten, “Lensfree microscopy: A new framework for the imaging of viruses, bacteria, cells and tissue,” in Proceedings of IEEE International Electron Devices Meeting (IEDM), (IEEE, 2015), p. 13.4.

41. B. E. Bayer, “Color imaging array,” (1976). US Patent 3,971,065.

42. J. Song, C. L. Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6(1), 24681 (2016). [CrossRef]  

43. C. Fournier, F. Jolivet, L. Denis, N. Verrier, E. Thiebaut, C. Allier, and T. Fournel, “Pixel super-resolution in digital holography by regularized reconstruction,” Appl. Opt. 56(1), 69–77 (2017). [CrossRef]  

44. Y. Zhang, T. Liu, Y. Huang, D. Teng, Y. Bian, Y. Wu, Y. Rivenson, A. Feizi, and A. Ozcan, “Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains,” J. Biophotonics 12(3), e201800335 (2019). [CrossRef]  

45. T. Liu, Z. Wei, Y. Rivenson, K. de Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019). [CrossRef]  

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References

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  1. D. Gabor, “A new microscopic principle,” Nature 161(4098), 777–778 (1948).
    [Crossref]
  2. J. Garcia-Sucerquia, W. Xu, S. K. Jericho, P. Klages, M. H. Jericho, and H. J. Kreuzer, “Digital in-line holographic microscopy,” Appl. Opt. 45(5), 836–850 (2006).
    [Crossref]
  3. S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012).
    [Crossref]
  4. O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
    [Crossref]
  5. G. Coppola, P. Ferraro, M. Iodice, S. De Nicola, A. Finizio, and S. Grilli, “A digital holographic microscope for complete characterization of microelectromechanical systems,” Meas. Sci. Technol. 15(3), 529–539 (2004).
    [Crossref]
  6. S. V. Kesavan, F. P. N. Y. Garcia, M. Menneteau, F. Mittler, B. David-Watine, N. Dubrulle, S. L. Shorte, B. Chalmond, J.-M. Dinten, and C. P. Allier, “Real-time label-free detection of dividing cells by means of lensfree video-microscopy,” J. Biomed. Opt. 19(3), 1 (2014).
    [Crossref]
  7. S. V. Kesavan, F. Momey, O. Cioni, B. David-Watine, N. Dubrulle, S. Shorte, E. Sulpice, D. Freida, B. Chalmond, J. Dinten, X. Gidrol, and C. Allier, “High-throughput monitoring of major cell functions by means of lensfree video microscopy,” Sci. Rep. 4(1), 5942 (2015).
    [Crossref]
  8. Y. Sung, N. Lue, B. Hamza, J. Martel, D. Irimia, R. R. Dasari, W. Choi, Z. Yaqoob, and P. So, “Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel,” Phys. Rev. Appl. 1(1), 014002 (2014).
    [Crossref]
  9. D. Vercruysse, A. Dusa, R. Stahl, G. Vanmeerbeeck, K. de Wijs, C. Liu, D. Prodanov, P. Peumans, and L. Lagae, “Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer,” Lab Chip 15(4), 1123–1132 (2015).
    [Crossref]
  10. E. Mathieu, C. D. Paul, R. Stahl, G. Vanmeerbeeck, V. Reumers, C. Liu, K. Konstantopoulos, and L. Lagae, “Time-lapse lens-free imaging of cell migration in diverse physical microenvironments,” Lab Chip 16(17), 3304–3316 (2016).
    [Crossref]
  11. C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017).
    [Crossref]
  12. T. Pauwelyn, R. Stahl, L. Mayo, X. Zheng, A. Lambrechts, S. Janssens, L. Lagae, V. Reumers, and D. Braeken, “Reflective lens-free imaging on high-density silicon microelectrode arrays for monitoring and evaluation of in vitro cardiac contractility,” Biomed. Opt. Express 9(4), 1827–1841 (2018).
    [Crossref]
  13. Y. Fang, N. Yu, Y. Jiang, and C. Dang, “High-precision lens-less flow cytometer on a chip,” Micromachines 9(5), 227 (2018).
    [Crossref]
  14. T. Latychevskaia and H.-W. Fink, “Solution to the twin image problem in holography,” Phys. Rev. Lett. 98(23), 233901 (2007).
    [Crossref]
  15. B. M. Hennelly, D. P. Kelly, N. Pandey, and D. S. Monaghan, “Review of twin reduction and twin removal techniques in holography,” in Proceedings of CIICT 2009, (National University of Ireland Maynooth, 2009), pp. 241–245.
  16. L. Denis, C. Fournier, T. Fournel, and C. Ducottet, “Twin-image noise reduction by phase retrieval in in-line digital holography,” Proc. SPIE 5914, 59140J (2005).
    [Crossref]
  17. J. R. Fienup, “Phase retrieval algorithms: a comparison,” Appl. Opt. 21(15), 2758–2769 (1982).
    [Crossref]
  18. L. Rong, Y. Li, S. Liu, W. Xiao, F. Pan, and D. Wang, “Iterative solution to twin image problem in in-line digital holography,” Opt. Lasers Eng. 51(5), 553–559 (2013).
    [Crossref]
  19. C. Guo, S. Liu, and J. T. Sheridan, “Iterative phase retrieval algorithms. i: optimization,” Appl. Opt. 54(15), 4698–4708 (2015).
    [Crossref]
  20. A. Greenbaum, U. Sikora, and A. Ozcan, “Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imaging,” Lab Chip 12(7), 1242–1245 (2012).
    [Crossref]
  21. Y. Rivenson, Y. Wu, H. Wang, Y. Zhang, A. Feizi, and A. Ozcan, “Sparsity-based multi-height phase recovery in holographic microscopy,” Sci. Rep. 6(1), 37862 (2016).
    [Crossref]
  22. R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
    [Crossref]
  23. A. Yurt, R. Stahl, G. Vanmeerbeeck, Z. Lin, M. Jayapala, and A. Lambrechts, “Towards practical cost-effective lens-free imaging,” Proc. SPIE 10055, 100550J (2017).
    [Crossref]
  24. P. Bao, F. Zhang, G. Pedrini, and W. Osten, “Phase retrieval using multiple illumination wavelengths,” Opt. Lett. 33(4), 309–311 (2008).
    [Crossref]
  25. Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light: Sci. Appl. 7(2), 17141 (2018).
    [Crossref]
  26. Z. Luo, A. Yurt, R. Stahl, A. Lambrechts, V. Reumers, D. Braeken, and L. Lagae, “Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks,” Opt. Express 27(10), 13581–13595 (2019).
    [Crossref]
  27. Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. De Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3d imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25–27 (2019).
    [Crossref]
  28. Y. Rivenson, Y. Wu, and A. Ozcan, “Deep learning in holography and coherent imaging,” Light: Sci. Appl. 8(1), 85–88 (2019).
    [Crossref]
  29. Z. Göröcs, L. Orzó, M. Kiss, V. Tóth, and S. Tökés, “In-line color digital holographic microscope for water quality measurements,” Proc. SPIE 7376, 737614 (2010).
    [Crossref]
  30. N. Demoli, D. Vukicevic, and M. Torzynski, “Dynamic digital holographic interferometry with three wavelengths,” Opt. Express 11(7), 767–774 (2003).
    [Crossref]
  31. J.-M. Desse, P. Picart, and P. Tankam, “Digital three-color holographic interferometry for flow analysis,” Opt. Express 16(8), 5471–5480 (2008).
    [Crossref]
  32. J. Kühn, T. Colomb, F. Montfort, F. Charrière, Y. Emery, E. Cuche, P. Marquet, and C. Depeursinge, “Real-time dual-wavelength digital holographic microscopy with a single hologram acquisition,” Opt. Express 15(12), 7231–7242 (2007).
    [Crossref]
  33. P. Tankam, Q. Song, M. Karray, J.-C. Li, J. M. Desse, and P. Picart, “Real-time three-sensitivity measurements based on three-color digital fresnel holographic interferometry,” Opt. Lett. 35(12), 2055–2057 (2010).
    [Crossref]
  34. T. Kiire, D. Barada, J.-I. Sugisaka, Y. Hayasaki, and T. Yatagai, “Color digital holography using a single monochromatic imaging sensor,” Opt. Lett. 37(15), 3153–3155 (2012).
    [Crossref]
  35. X. Mo, B. Kemper, P. Langehanenberg, A. Vollmer, J. Xie, and G. von Bally, “Application of color digital holographic microscopy for analysis of stained tissue sections,” in European Conference on Biomedical Optics, (Optical Society of America, 2009). Paper 7367_18.
  36. E. L. Clarke and D. Treanor, “Colour in digital pathology: a review,” Histopathology 70(2), 153–163 (2017).
    [Crossref]
  37. P. Shrestha and B. Hulsken, “Color accuracy and reproducibility in whole slide imaging scanners,” J. Med. Imag. 1(2), 027501 (2014).
    [Crossref]
  38. P. Xia, Y. Ito, Y. Shimozato, T. Tahara, T. Kakue, Y. Awatsuji, K. Nishio, S. Ura, T. Kubota, and O. Matoba, “Digital holography using spectral estimation technique,” J. Disp. Technol. 10(3), 235–242 (2014).
  39. S. O. Isikman, I. Sencan, O. Mudanyali, W. Bishara, C. Oztoprak, and A. Ozcan, “Color and monochrome lensless on-chip imaging of caenorhabditis elegans over a wide field-of-view,” Lab Chip 10(9), 1109–1112 (2010).
    [Crossref]
  40. C. Allier, S. V. Kesavan, Y. Hennequin, O. Cioni, F. Momey, T. Bordy, L. Herve, J.-G. Coutard, S. Morel, A. Berdeu, F. Navarro, and J.-M. Dinten, “Lensfree microscopy: A new framework for the imaging of viruses, bacteria, cells and tissue,” in Proceedings of IEEE International Electron Devices Meeting (IEDM), (IEEE, 2015), p. 13.4.
  41. B. E. Bayer, “Color imaging array,” (1976). US Patent 3,971,065.
  42. J. Song, C. L. Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6(1), 24681 (2016).
    [Crossref]
  43. C. Fournier, F. Jolivet, L. Denis, N. Verrier, E. Thiebaut, C. Allier, and T. Fournel, “Pixel super-resolution in digital holography by regularized reconstruction,” Appl. Opt. 56(1), 69–77 (2017).
    [Crossref]
  44. Y. Zhang, T. Liu, Y. Huang, D. Teng, Y. Bian, Y. Wu, Y. Rivenson, A. Feizi, and A. Ozcan, “Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains,” J. Biophotonics 12(3), e201800335 (2019).
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2019 (5)

Z. Luo, A. Yurt, R. Stahl, A. Lambrechts, V. Reumers, D. Braeken, and L. Lagae, “Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks,” Opt. Express 27(10), 13581–13595 (2019).
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Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. De Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3d imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25–27 (2019).
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Y. Rivenson, Y. Wu, and A. Ozcan, “Deep learning in holography and coherent imaging,” Light: Sci. Appl. 8(1), 85–88 (2019).
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Y. Zhang, T. Liu, Y. Huang, D. Teng, Y. Bian, Y. Wu, Y. Rivenson, A. Feizi, and A. Ozcan, “Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains,” J. Biophotonics 12(3), e201800335 (2019).
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T. Liu, Z. Wei, Y. Rivenson, K. de Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019).
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2018 (3)

Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light: Sci. Appl. 7(2), 17141 (2018).
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T. Pauwelyn, R. Stahl, L. Mayo, X. Zheng, A. Lambrechts, S. Janssens, L. Lagae, V. Reumers, and D. Braeken, “Reflective lens-free imaging on high-density silicon microelectrode arrays for monitoring and evaluation of in vitro cardiac contractility,” Biomed. Opt. Express 9(4), 1827–1841 (2018).
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Y. Fang, N. Yu, Y. Jiang, and C. Dang, “High-precision lens-less flow cytometer on a chip,” Micromachines 9(5), 227 (2018).
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2017 (4)

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017).
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E. L. Clarke and D. Treanor, “Colour in digital pathology: a review,” Histopathology 70(2), 153–163 (2017).
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A. Yurt, R. Stahl, G. Vanmeerbeeck, Z. Lin, M. Jayapala, and A. Lambrechts, “Towards practical cost-effective lens-free imaging,” Proc. SPIE 10055, 100550J (2017).
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C. Fournier, F. Jolivet, L. Denis, N. Verrier, E. Thiebaut, C. Allier, and T. Fournel, “Pixel super-resolution in digital holography by regularized reconstruction,” Appl. Opt. 56(1), 69–77 (2017).
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2016 (3)

J. Song, C. L. Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6(1), 24681 (2016).
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Y. Rivenson, Y. Wu, H. Wang, Y. Zhang, A. Feizi, and A. Ozcan, “Sparsity-based multi-height phase recovery in holographic microscopy,” Sci. Rep. 6(1), 37862 (2016).
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E. Mathieu, C. D. Paul, R. Stahl, G. Vanmeerbeeck, V. Reumers, C. Liu, K. Konstantopoulos, and L. Lagae, “Time-lapse lens-free imaging of cell migration in diverse physical microenvironments,” Lab Chip 16(17), 3304–3316 (2016).
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2015 (5)

C. Guo, S. Liu, and J. T. Sheridan, “Iterative phase retrieval algorithms. i: optimization,” Appl. Opt. 54(15), 4698–4708 (2015).
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S. V. Kesavan, F. Momey, O. Cioni, B. David-Watine, N. Dubrulle, S. Shorte, E. Sulpice, D. Freida, B. Chalmond, J. Dinten, X. Gidrol, and C. Allier, “High-throughput monitoring of major cell functions by means of lensfree video microscopy,” Sci. Rep. 4(1), 5942 (2015).
[Crossref]

D. Vercruysse, A. Dusa, R. Stahl, G. Vanmeerbeeck, K. de Wijs, C. Liu, D. Prodanov, P. Peumans, and L. Lagae, “Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer,” Lab Chip 15(4), 1123–1132 (2015).
[Crossref]

T. Latychevskaia and H.-W. Fink, “Practical algorithms for simulation and reconstruction of digital in-line holograms,” Appl. Opt. 54(9), 2424–2434 (2015).
[Crossref]

T. Latychevskaia and H.-W. Fink, “Reconstruction of purely absorbing, absorbing and phase-shifting, and strong phase-shifting objects from their single-shot in-line holograms,” Appl. Opt. 54(13), 3925–3932 (2015).
[Crossref]

2014 (6)

P. A. Bautista, N. Hashimoto, and Y. Yagi, “Color standardization in whole slide imaging using a color calibration slide,” J. Pathol. Inform. 5(1), 4 (2014).
[Crossref]

S. V. Kesavan, F. P. N. Y. Garcia, M. Menneteau, F. Mittler, B. David-Watine, N. Dubrulle, S. L. Shorte, B. Chalmond, J.-M. Dinten, and C. P. Allier, “Real-time label-free detection of dividing cells by means of lensfree video-microscopy,” J. Biomed. Opt. 19(3), 1 (2014).
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Y. Sung, N. Lue, B. Hamza, J. Martel, D. Irimia, R. R. Dasari, W. Choi, Z. Yaqoob, and P. So, “Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel,” Phys. Rev. Appl. 1(1), 014002 (2014).
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R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
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P. Shrestha and B. Hulsken, “Color accuracy and reproducibility in whole slide imaging scanners,” J. Med. Imag. 1(2), 027501 (2014).
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P. Xia, Y. Ito, Y. Shimozato, T. Tahara, T. Kakue, Y. Awatsuji, K. Nishio, S. Ura, T. Kubota, and O. Matoba, “Digital holography using spectral estimation technique,” J. Disp. Technol. 10(3), 235–242 (2014).

2013 (2)

L. Rong, Y. Li, S. Liu, W. Xiao, F. Pan, and D. Wang, “Iterative solution to twin image problem in in-line digital holography,” Opt. Lasers Eng. 51(5), 553–559 (2013).
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D. G. Stavenga, H. L. Leertouwer, and B. D. Wilts, “Quantifying the refractive index dispersion of a pigmented biological tissue using jamin–lebedeff interference microscopy,” Light: Sci. Appl. 2(9), e100 (2013).
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2012 (4)

L. Cherkezyan, H. Subramanian, V. Stoyneva, J. D. Rogers, S. Yang, D. Damania, A. Taflove, and V. Backman, “Targeted alteration of real and imaginary refractive index of biological cells by histological staining,” Opt. Lett. 37(10), 1601–1603 (2012).
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T. Kiire, D. Barada, J.-I. Sugisaka, Y. Hayasaki, and T. Yatagai, “Color digital holography using a single monochromatic imaging sensor,” Opt. Lett. 37(15), 3153–3155 (2012).
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S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012).
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A. Greenbaum, U. Sikora, and A. Ozcan, “Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imaging,” Lab Chip 12(7), 1242–1245 (2012).
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2010 (5)

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
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S. O. Isikman, I. Sencan, O. Mudanyali, W. Bishara, C. Oztoprak, and A. Ozcan, “Color and monochrome lensless on-chip imaging of caenorhabditis elegans over a wide field-of-view,” Lab Chip 10(9), 1109–1112 (2010).
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Z. Göröcs, L. Orzó, M. Kiss, V. Tóth, and S. Tökés, “In-line color digital holographic microscope for water quality measurements,” Proc. SPIE 7376, 737614 (2010).
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P. Tankam, Q. Song, M. Karray, J.-C. Li, J. M. Desse, and P. Picart, “Real-time three-sensitivity measurements based on three-color digital fresnel holographic interferometry,” Opt. Lett. 35(12), 2055–2057 (2010).
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Á. F. Doval and C. Trillo, “Dimensionless formulation of the convolution and angular spectrum reconstruction methods in digital holography,” Proc. SPIE 7387, 73870U (2010).
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2008 (2)

2007 (2)

2006 (2)

J. Garcia-Sucerquia, W. Xu, S. K. Jericho, P. Klages, M. H. Jericho, and H. J. Kreuzer, “Digital in-line holographic microscopy,” Appl. Opt. 45(5), 836–850 (2006).
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M. K. Kim, L. Yu, and C. J. Mann, “Interference techniques in digital holography,” J. Opt. A: Pure Appl. Opt. 8(7), S518–S523 (2006).
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2005 (1)

L. Denis, C. Fournier, T. Fournel, and C. Ducottet, “Twin-image noise reduction by phase retrieval in in-line digital holography,” Proc. SPIE 5914, 59140J (2005).
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2004 (2)

G. Coppola, P. Ferraro, M. Iodice, S. De Nicola, A. Finizio, and S. Grilli, “A digital holographic microscope for complete characterization of microelectromechanical systems,” Meas. Sci. Technol. 15(3), 529–539 (2004).
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Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
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2003 (1)

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C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017).
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C. Fournier, F. Jolivet, L. Denis, N. Verrier, E. Thiebaut, C. Allier, and T. Fournel, “Pixel super-resolution in digital holography by regularized reconstruction,” Appl. Opt. 56(1), 69–77 (2017).
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S. V. Kesavan, F. Momey, O. Cioni, B. David-Watine, N. Dubrulle, S. Shorte, E. Sulpice, D. Freida, B. Chalmond, J. Dinten, X. Gidrol, and C. Allier, “High-throughput monitoring of major cell functions by means of lensfree video microscopy,” Sci. Rep. 4(1), 5942 (2015).
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Allier, C. P.

S. V. Kesavan, F. P. N. Y. Garcia, M. Menneteau, F. Mittler, B. David-Watine, N. Dubrulle, S. L. Shorte, B. Chalmond, J.-M. Dinten, and C. P. Allier, “Real-time label-free detection of dividing cells by means of lensfree video-microscopy,” J. Biomed. Opt. 19(3), 1 (2014).
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Awatsuji, Y.

P. Xia, Y. Ito, Y. Shimozato, T. Tahara, T. Kakue, Y. Awatsuji, K. Nishio, S. Ura, T. Kubota, and O. Matoba, “Digital holography using spectral estimation technique,” J. Disp. Technol. 10(3), 235–242 (2014).

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P. A. Bautista, N. Hashimoto, and Y. Yagi, “Color standardization in whole slide imaging using a color calibration slide,” J. Pathol. Inform. 5(1), 4 (2014).
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C. Allier, S. V. Kesavan, Y. Hennequin, O. Cioni, F. Momey, T. Bordy, L. Herve, J.-G. Coutard, S. Morel, A. Berdeu, F. Navarro, and J.-M. Dinten, “Lensfree microscopy: A new framework for the imaging of viruses, bacteria, cells and tissue,” in Proceedings of IEEE International Electron Devices Meeting (IEDM), (IEEE, 2015), p. 13.4.

Bian, Y.

Y. Zhang, T. Liu, Y. Huang, D. Teng, Y. Bian, Y. Wu, Y. Rivenson, A. Feizi, and A. Ozcan, “Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains,” J. Biophotonics 12(3), e201800335 (2019).
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S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012).
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O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
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S. O. Isikman, I. Sencan, O. Mudanyali, W. Bishara, C. Oztoprak, and A. Ozcan, “Color and monochrome lensless on-chip imaging of caenorhabditis elegans over a wide field-of-view,” Lab Chip 10(9), 1109–1112 (2010).
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C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017).
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C. Allier, S. V. Kesavan, Y. Hennequin, O. Cioni, F. Momey, T. Bordy, L. Herve, J.-G. Coutard, S. Morel, A. Berdeu, F. Navarro, and J.-M. Dinten, “Lensfree microscopy: A new framework for the imaging of viruses, bacteria, cells and tissue,” in Proceedings of IEEE International Electron Devices Meeting (IEDM), (IEEE, 2015), p. 13.4.

Bovik, A. C.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
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Braeken, D.

Brettle, D.

W. C. Revie, M. Shires, P. Jackson, D. Brettle, R. Cochrane, and D. Treanor, “Color management in digital pathology,” in Analytical Cellular Pathology, vol. 2014 (Hindawi, 2014).

Calis, A.

Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. De Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3d imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25–27 (2019).
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Carazo, J. M.

I. Arganda-Carreras, C. O. Sorzano, R. Marabini, J. M. Carazo, C. Ortiz-de Solorzano, and J. Kybic, “Consistent and elastic registration of histological sections using vector-spline regularization,” in International Workshop on Computer Vision Approaches to Medical Image Analysis, (Springer, 2006), pp. 85–95.

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S. V. Kesavan, F. Momey, O. Cioni, B. David-Watine, N. Dubrulle, S. Shorte, E. Sulpice, D. Freida, B. Chalmond, J. Dinten, X. Gidrol, and C. Allier, “High-throughput monitoring of major cell functions by means of lensfree video microscopy,” Sci. Rep. 4(1), 5942 (2015).
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S. V. Kesavan, F. P. N. Y. Garcia, M. Menneteau, F. Mittler, B. David-Watine, N. Dubrulle, S. L. Shorte, B. Chalmond, J.-M. Dinten, and C. P. Allier, “Real-time label-free detection of dividing cells by means of lensfree video-microscopy,” J. Biomed. Opt. 19(3), 1 (2014).
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Chaudhari, G.

Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. De Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3d imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25–27 (2019).
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Choi, W.

Y. Sung, N. Lue, B. Hamza, J. Martel, D. Irimia, R. R. Dasari, W. Choi, Z. Yaqoob, and P. So, “Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel,” Phys. Rev. Appl. 1(1), 014002 (2014).
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C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017).
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S. V. Kesavan, F. Momey, O. Cioni, B. David-Watine, N. Dubrulle, S. Shorte, E. Sulpice, D. Freida, B. Chalmond, J. Dinten, X. Gidrol, and C. Allier, “High-throughput monitoring of major cell functions by means of lensfree video microscopy,” Sci. Rep. 4(1), 5942 (2015).
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C. Allier, S. V. Kesavan, Y. Hennequin, O. Cioni, F. Momey, T. Bordy, L. Herve, J.-G. Coutard, S. Morel, A. Berdeu, F. Navarro, and J.-M. Dinten, “Lensfree microscopy: A new framework for the imaging of viruses, bacteria, cells and tissue,” in Proceedings of IEEE International Electron Devices Meeting (IEDM), (IEEE, 2015), p. 13.4.

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E. L. Clarke and D. Treanor, “Colour in digital pathology: a review,” Histopathology 70(2), 153–163 (2017).
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W. C. Revie, M. Shires, P. Jackson, D. Brettle, R. Cochrane, and D. Treanor, “Color management in digital pathology,” in Analytical Cellular Pathology, vol. 2014 (Hindawi, 2014).

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Coppola, G.

G. Coppola, P. Ferraro, M. Iodice, S. De Nicola, A. Finizio, and S. Grilli, “A digital holographic microscope for complete characterization of microelectromechanical systems,” Meas. Sci. Technol. 15(3), 529–539 (2004).
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C. Allier, S. V. Kesavan, Y. Hennequin, O. Cioni, F. Momey, T. Bordy, L. Herve, J.-G. Coutard, S. Morel, A. Berdeu, F. Navarro, and J.-M. Dinten, “Lensfree microscopy: A new framework for the imaging of viruses, bacteria, cells and tissue,” in Proceedings of IEEE International Electron Devices Meeting (IEDM), (IEEE, 2015), p. 13.4.

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Y. Fang, N. Yu, Y. Jiang, and C. Dang, “High-precision lens-less flow cytometer on a chip,” Micromachines 9(5), 227 (2018).
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Y. Sung, N. Lue, B. Hamza, J. Martel, D. Irimia, R. R. Dasari, W. Choi, Z. Yaqoob, and P. So, “Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel,” Phys. Rev. Appl. 1(1), 014002 (2014).
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S. V. Kesavan, F. Momey, O. Cioni, B. David-Watine, N. Dubrulle, S. Shorte, E. Sulpice, D. Freida, B. Chalmond, J. Dinten, X. Gidrol, and C. Allier, “High-throughput monitoring of major cell functions by means of lensfree video microscopy,” Sci. Rep. 4(1), 5942 (2015).
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S. V. Kesavan, F. P. N. Y. Garcia, M. Menneteau, F. Mittler, B. David-Watine, N. Dubrulle, S. L. Shorte, B. Chalmond, J.-M. Dinten, and C. P. Allier, “Real-time label-free detection of dividing cells by means of lensfree video-microscopy,” J. Biomed. Opt. 19(3), 1 (2014).
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Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. De Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3d imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25–27 (2019).
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T. Liu, Z. Wei, Y. Rivenson, K. de Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019).
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G. Coppola, P. Ferraro, M. Iodice, S. De Nicola, A. Finizio, and S. Grilli, “A digital holographic microscope for complete characterization of microelectromechanical systems,” Meas. Sci. Technol. 15(3), 529–539 (2004).
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[Crossref]

D. Vercruysse, A. Dusa, R. Stahl, G. Vanmeerbeeck, K. de Wijs, C. Liu, D. Prodanov, P. Peumans, and L. Lagae, “Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer,” Lab Chip 15(4), 1123–1132 (2015).
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Liu, S.

C. Guo, S. Liu, and J. T. Sheridan, “Iterative phase retrieval algorithms. i: optimization,” Appl. Opt. 54(15), 4698–4708 (2015).
[Crossref]

L. Rong, Y. Li, S. Liu, W. Xiao, F. Pan, and D. Wang, “Iterative solution to twin image problem in in-line digital holography,” Opt. Lasers Eng. 51(5), 553–559 (2013).
[Crossref]

Liu, T.

Y. Zhang, T. Liu, Y. Huang, D. Teng, Y. Bian, Y. Wu, Y. Rivenson, A. Feizi, and A. Ozcan, “Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains,” J. Biophotonics 12(3), e201800335 (2019).
[Crossref]

T. Liu, Z. Wei, Y. Rivenson, K. de Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019).
[Crossref]

Lue, N.

Y. Sung, N. Lue, B. Hamza, J. Martel, D. Irimia, R. R. Dasari, W. Choi, Z. Yaqoob, and P. So, “Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel,” Phys. Rev. Appl. 1(1), 014002 (2014).
[Crossref]

Luo, Y.

Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. De Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3d imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25–27 (2019).
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Luo, Z.

Mann, C. J.

M. K. Kim, L. Yu, and C. J. Mann, “Interference techniques in digital holography,” J. Opt. A: Pure Appl. Opt. 8(7), S518–S523 (2006).
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Marabini, R.

I. Arganda-Carreras, C. O. Sorzano, R. Marabini, J. M. Carazo, C. Ortiz-de Solorzano, and J. Kybic, “Consistent and elastic registration of histological sections using vector-spline regularization,” in International Workshop on Computer Vision Approaches to Medical Image Analysis, (Springer, 2006), pp. 85–95.

Marquet, P.

Martel, J.

Y. Sung, N. Lue, B. Hamza, J. Martel, D. Irimia, R. R. Dasari, W. Choi, Z. Yaqoob, and P. So, “Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel,” Phys. Rev. Appl. 1(1), 014002 (2014).
[Crossref]

Mathieu, E.

E. Mathieu, C. D. Paul, R. Stahl, G. Vanmeerbeeck, V. Reumers, C. Liu, K. Konstantopoulos, and L. Lagae, “Time-lapse lens-free imaging of cell migration in diverse physical microenvironments,” Lab Chip 16(17), 3304–3316 (2016).
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Matoba, O.

P. Xia, Y. Ito, Y. Shimozato, T. Tahara, T. Kakue, Y. Awatsuji, K. Nishio, S. Ura, T. Kubota, and O. Matoba, “Digital holography using spectral estimation technique,” J. Disp. Technol. 10(3), 235–242 (2014).

Mayo, L.

Menneteau, M.

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017).
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S. V. Kesavan, F. P. N. Y. Garcia, M. Menneteau, F. Mittler, B. David-Watine, N. Dubrulle, S. L. Shorte, B. Chalmond, J.-M. Dinten, and C. P. Allier, “Real-time label-free detection of dividing cells by means of lensfree video-microscopy,” J. Biomed. Opt. 19(3), 1 (2014).
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Mittler, F.

S. V. Kesavan, F. P. N. Y. Garcia, M. Menneteau, F. Mittler, B. David-Watine, N. Dubrulle, S. L. Shorte, B. Chalmond, J.-M. Dinten, and C. P. Allier, “Real-time label-free detection of dividing cells by means of lensfree video-microscopy,” J. Biomed. Opt. 19(3), 1 (2014).
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Mo, X.

X. Mo, B. Kemper, P. Langehanenberg, A. Vollmer, J. Xie, and G. von Bally, “Application of color digital holographic microscopy for analysis of stained tissue sections,” in European Conference on Biomedical Optics, (Optical Society of America, 2009). Paper 7367_18.

Momey, F.

S. V. Kesavan, F. Momey, O. Cioni, B. David-Watine, N. Dubrulle, S. Shorte, E. Sulpice, D. Freida, B. Chalmond, J. Dinten, X. Gidrol, and C. Allier, “High-throughput monitoring of major cell functions by means of lensfree video microscopy,” Sci. Rep. 4(1), 5942 (2015).
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C. Allier, S. V. Kesavan, Y. Hennequin, O. Cioni, F. Momey, T. Bordy, L. Herve, J.-G. Coutard, S. Morel, A. Berdeu, F. Navarro, and J.-M. Dinten, “Lensfree microscopy: A new framework for the imaging of viruses, bacteria, cells and tissue,” in Proceedings of IEEE International Electron Devices Meeting (IEDM), (IEEE, 2015), p. 13.4.

Monaghan, D. S.

B. M. Hennelly, D. P. Kelly, N. Pandey, and D. S. Monaghan, “Review of twin reduction and twin removal techniques in holography,” in Proceedings of CIICT 2009, (National University of Ireland Maynooth, 2009), pp. 241–245.

Montfort, F.

Morel, S.

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017).
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C. Allier, S. V. Kesavan, Y. Hennequin, O. Cioni, F. Momey, T. Bordy, L. Herve, J.-G. Coutard, S. Morel, A. Berdeu, F. Navarro, and J.-M. Dinten, “Lensfree microscopy: A new framework for the imaging of viruses, bacteria, cells and tissue,” in Proceedings of IEEE International Electron Devices Meeting (IEDM), (IEEE, 2015), p. 13.4.

Mudanyali, O.

S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012).
[Crossref]

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref]

S. O. Isikman, I. Sencan, O. Mudanyali, W. Bishara, C. Oztoprak, and A. Ozcan, “Color and monochrome lensless on-chip imaging of caenorhabditis elegans over a wide field-of-view,” Lab Chip 10(9), 1109–1112 (2010).
[Crossref]

Nagata, T.

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
[Crossref]

Navarro, F.

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017).
[Crossref]

C. Allier, S. V. Kesavan, Y. Hennequin, O. Cioni, F. Momey, T. Bordy, L. Herve, J.-G. Coutard, S. Morel, A. Berdeu, F. Navarro, and J.-M. Dinten, “Lensfree microscopy: A new framework for the imaging of viruses, bacteria, cells and tissue,” in Proceedings of IEEE International Electron Devices Meeting (IEDM), (IEEE, 2015), p. 13.4.

Nishio, K.

P. Xia, Y. Ito, Y. Shimozato, T. Tahara, T. Kakue, Y. Awatsuji, K. Nishio, S. Ura, T. Kubota, and O. Matoba, “Digital holography using spectral estimation technique,” J. Disp. Technol. 10(3), 235–242 (2014).

Nishishita, N.

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
[Crossref]

Oh, C.

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref]

Ortiz-de Solorzano, C.

I. Arganda-Carreras, C. O. Sorzano, R. Marabini, J. M. Carazo, C. Ortiz-de Solorzano, and J. Kybic, “Consistent and elastic registration of histological sections using vector-spline regularization,” in International Workshop on Computer Vision Approaches to Medical Image Analysis, (Springer, 2006), pp. 85–95.

Orzó, L.

Z. Göröcs, L. Orzó, M. Kiss, V. Tóth, and S. Tökés, “In-line color digital holographic microscope for water quality measurements,” Proc. SPIE 7376, 737614 (2010).
[Crossref]

Oshima, Y.

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
[Crossref]

Osten, W.

Ozaki, S.

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
[Crossref]

Ozcan, A.

Y. Rivenson, Y. Wu, and A. Ozcan, “Deep learning in holography and coherent imaging,” Light: Sci. Appl. 8(1), 85–88 (2019).
[Crossref]

Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. De Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3d imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25–27 (2019).
[Crossref]

Y. Zhang, T. Liu, Y. Huang, D. Teng, Y. Bian, Y. Wu, Y. Rivenson, A. Feizi, and A. Ozcan, “Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains,” J. Biophotonics 12(3), e201800335 (2019).
[Crossref]

T. Liu, Z. Wei, Y. Rivenson, K. de Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019).
[Crossref]

Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light: Sci. Appl. 7(2), 17141 (2018).
[Crossref]

Y. Rivenson, Y. Wu, H. Wang, Y. Zhang, A. Feizi, and A. Ozcan, “Sparsity-based multi-height phase recovery in holographic microscopy,” Sci. Rep. 6(1), 37862 (2016).
[Crossref]

A. Greenbaum, U. Sikora, and A. Ozcan, “Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imaging,” Lab Chip 12(7), 1242–1245 (2012).
[Crossref]

S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012).
[Crossref]

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref]

S. O. Isikman, I. Sencan, O. Mudanyali, W. Bishara, C. Oztoprak, and A. Ozcan, “Color and monochrome lensless on-chip imaging of caenorhabditis elegans over a wide field-of-view,” Lab Chip 10(9), 1109–1112 (2010).
[Crossref]

Oztoprak, C.

S. O. Isikman, I. Sencan, O. Mudanyali, W. Bishara, C. Oztoprak, and A. Ozcan, “Color and monochrome lensless on-chip imaging of caenorhabditis elegans over a wide field-of-view,” Lab Chip 10(9), 1109–1112 (2010).
[Crossref]

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref]

Pan, F.

L. Rong, Y. Li, S. Liu, W. Xiao, F. Pan, and D. Wang, “Iterative solution to twin image problem in in-line digital holography,” Opt. Lasers Eng. 51(5), 553–559 (2013).
[Crossref]

Pandey, N.

B. M. Hennelly, D. P. Kelly, N. Pandey, and D. S. Monaghan, “Review of twin reduction and twin removal techniques in holography,” in Proceedings of CIICT 2009, (National University of Ireland Maynooth, 2009), pp. 241–245.

Pathania, D.

J. Song, C. L. Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6(1), 24681 (2016).
[Crossref]

Paul, C. D.

E. Mathieu, C. D. Paul, R. Stahl, G. Vanmeerbeeck, V. Reumers, C. Liu, K. Konstantopoulos, and L. Lagae, “Time-lapse lens-free imaging of cell migration in diverse physical microenvironments,” Lab Chip 16(17), 3304–3316 (2016).
[Crossref]

Pauwelyn, T.

Pedrini, G.

Peumans, P.

D. Vercruysse, A. Dusa, R. Stahl, G. Vanmeerbeeck, K. de Wijs, C. Liu, D. Prodanov, P. Peumans, and L. Lagae, “Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer,” Lab Chip 15(4), 1123–1132 (2015).
[Crossref]

Picart, P.

Pivovarov, M.

J. Song, C. L. Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6(1), 24681 (2016).
[Crossref]

Prodanov, D.

D. Vercruysse, A. Dusa, R. Stahl, G. Vanmeerbeeck, K. de Wijs, C. Liu, D. Prodanov, P. Peumans, and L. Lagae, “Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer,” Lab Chip 15(4), 1123–1132 (2015).
[Crossref]

Reumers, V.

Z. Luo, A. Yurt, R. Stahl, A. Lambrechts, V. Reumers, D. Braeken, and L. Lagae, “Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks,” Opt. Express 27(10), 13581–13595 (2019).
[Crossref]

T. Pauwelyn, R. Stahl, L. Mayo, X. Zheng, A. Lambrechts, S. Janssens, L. Lagae, V. Reumers, and D. Braeken, “Reflective lens-free imaging on high-density silicon microelectrode arrays for monitoring and evaluation of in vitro cardiac contractility,” Biomed. Opt. Express 9(4), 1827–1841 (2018).
[Crossref]

E. Mathieu, C. D. Paul, R. Stahl, G. Vanmeerbeeck, V. Reumers, C. Liu, K. Konstantopoulos, and L. Lagae, “Time-lapse lens-free imaging of cell migration in diverse physical microenvironments,” Lab Chip 16(17), 3304–3316 (2016).
[Crossref]

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
[Crossref]

Revie, W. C.

W. C. Revie, M. Shires, P. Jackson, D. Brettle, R. Cochrane, and D. Treanor, “Color management in digital pathology,” in Analytical Cellular Pathology, vol. 2014 (Hindawi, 2014).

Rivenson, Y.

Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. De Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3d imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25–27 (2019).
[Crossref]

Y. Rivenson, Y. Wu, and A. Ozcan, “Deep learning in holography and coherent imaging,” Light: Sci. Appl. 8(1), 85–88 (2019).
[Crossref]

Y. Zhang, T. Liu, Y. Huang, D. Teng, Y. Bian, Y. Wu, Y. Rivenson, A. Feizi, and A. Ozcan, “Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains,” J. Biophotonics 12(3), e201800335 (2019).
[Crossref]

T. Liu, Z. Wei, Y. Rivenson, K. de Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019).
[Crossref]

Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light: Sci. Appl. 7(2), 17141 (2018).
[Crossref]

Y. Rivenson, Y. Wu, H. Wang, Y. Zhang, A. Feizi, and A. Ozcan, “Sparsity-based multi-height phase recovery in holographic microscopy,” Sci. Rep. 6(1), 37862 (2016).
[Crossref]

Rogers, J. D.

Rong, L.

L. Rong, Y. Li, S. Liu, W. Xiao, F. Pan, and D. Wang, “Iterative solution to twin image problem in in-line digital holography,” Opt. Lasers Eng. 51(5), 553–559 (2013).
[Crossref]

Sencan, I.

S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012).
[Crossref]

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref]

S. O. Isikman, I. Sencan, O. Mudanyali, W. Bishara, C. Oztoprak, and A. Ozcan, “Color and monochrome lensless on-chip imaging of caenorhabditis elegans over a wide field-of-view,” Lab Chip 10(9), 1109–1112 (2010).
[Crossref]

Seo, S.

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref]

Sheikh, H. R.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
[Crossref]

Sheridan, J. T.

Shimozato, Y.

P. Xia, Y. Ito, Y. Shimozato, T. Tahara, T. Kakue, Y. Awatsuji, K. Nishio, S. Ura, T. Kubota, and O. Matoba, “Digital holography using spectral estimation technique,” J. Disp. Technol. 10(3), 235–242 (2014).

Shires, M.

W. C. Revie, M. Shires, P. Jackson, D. Brettle, R. Cochrane, and D. Treanor, “Color management in digital pathology,” in Analytical Cellular Pathology, vol. 2014 (Hindawi, 2014).

Shorte, S.

S. V. Kesavan, F. Momey, O. Cioni, B. David-Watine, N. Dubrulle, S. Shorte, E. Sulpice, D. Freida, B. Chalmond, J. Dinten, X. Gidrol, and C. Allier, “High-throughput monitoring of major cell functions by means of lensfree video microscopy,” Sci. Rep. 4(1), 5942 (2015).
[Crossref]

Shorte, S. L.

S. V. Kesavan, F. P. N. Y. Garcia, M. Menneteau, F. Mittler, B. David-Watine, N. Dubrulle, S. L. Shorte, B. Chalmond, J.-M. Dinten, and C. P. Allier, “Real-time label-free detection of dividing cells by means of lensfree video-microscopy,” J. Biomed. Opt. 19(3), 1 (2014).
[Crossref]

Shrestha, P.

P. Shrestha and B. Hulsken, “Color accuracy and reproducibility in whole slide imaging scanners,” J. Med. Imag. 1(2), 027501 (2014).
[Crossref]

Sikora, U.

S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012).
[Crossref]

A. Greenbaum, U. Sikora, and A. Ozcan, “Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imaging,” Lab Chip 12(7), 1242–1245 (2012).
[Crossref]

Simoncelli, E. P.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
[Crossref]

So, P.

Y. Sung, N. Lue, B. Hamza, J. Martel, D. Irimia, R. R. Dasari, W. Choi, Z. Yaqoob, and P. So, “Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel,” Phys. Rev. Appl. 1(1), 014002 (2014).
[Crossref]

Song, J.

J. Song, C. L. Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6(1), 24681 (2016).
[Crossref]

Song, Q.

Sorzano, C. O.

I. Arganda-Carreras, C. O. Sorzano, R. Marabini, J. M. Carazo, C. Ortiz-de Solorzano, and J. Kybic, “Consistent and elastic registration of histological sections using vector-spline regularization,” in International Workshop on Computer Vision Approaches to Medical Image Analysis, (Springer, 2006), pp. 85–95.

Stahl, R.

Z. Luo, A. Yurt, R. Stahl, A. Lambrechts, V. Reumers, D. Braeken, and L. Lagae, “Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks,” Opt. Express 27(10), 13581–13595 (2019).
[Crossref]

T. Pauwelyn, R. Stahl, L. Mayo, X. Zheng, A. Lambrechts, S. Janssens, L. Lagae, V. Reumers, and D. Braeken, “Reflective lens-free imaging on high-density silicon microelectrode arrays for monitoring and evaluation of in vitro cardiac contractility,” Biomed. Opt. Express 9(4), 1827–1841 (2018).
[Crossref]

A. Yurt, R. Stahl, G. Vanmeerbeeck, Z. Lin, M. Jayapala, and A. Lambrechts, “Towards practical cost-effective lens-free imaging,” Proc. SPIE 10055, 100550J (2017).
[Crossref]

E. Mathieu, C. D. Paul, R. Stahl, G. Vanmeerbeeck, V. Reumers, C. Liu, K. Konstantopoulos, and L. Lagae, “Time-lapse lens-free imaging of cell migration in diverse physical microenvironments,” Lab Chip 16(17), 3304–3316 (2016).
[Crossref]

D. Vercruysse, A. Dusa, R. Stahl, G. Vanmeerbeeck, K. de Wijs, C. Liu, D. Prodanov, P. Peumans, and L. Lagae, “Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer,” Lab Chip 15(4), 1123–1132 (2015).
[Crossref]

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
[Crossref]

Stavenga, D. G.

D. G. Stavenga, H. L. Leertouwer, and B. D. Wilts, “Quantifying the refractive index dispersion of a pigmented biological tissue using jamin–lebedeff interference microscopy,” Light: Sci. Appl. 2(9), e100 (2013).
[Crossref]

Stoyneva, V.

Su, T.-W.

S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012).
[Crossref]

Subramanian, H.

Sugisaka, J.-I.

Sulpice, E.

S. V. Kesavan, F. Momey, O. Cioni, B. David-Watine, N. Dubrulle, S. Shorte, E. Sulpice, D. Freida, B. Chalmond, J. Dinten, X. Gidrol, and C. Allier, “High-throughput monitoring of major cell functions by means of lensfree video microscopy,” Sci. Rep. 4(1), 5942 (2015).
[Crossref]

Sung, Y.

Y. Sung, N. Lue, B. Hamza, J. Martel, D. Irimia, R. R. Dasari, W. Choi, Z. Yaqoob, and P. So, “Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel,” Phys. Rev. Appl. 1(1), 014002 (2014).
[Crossref]

Swisher, C. L.

J. Song, C. L. Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6(1), 24681 (2016).
[Crossref]

Taflove, A.

Tahara, T.

P. Xia, Y. Ito, Y. Shimozato, T. Tahara, T. Kakue, Y. Awatsuji, K. Nishio, S. Ura, T. Kubota, and O. Matoba, “Digital holography using spectral estimation technique,” J. Disp. Technol. 10(3), 235–242 (2014).

Takemoto, M.

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
[Crossref]

Tankam, P.

Teng, D.

Y. Zhang, T. Liu, Y. Huang, D. Teng, Y. Bian, Y. Wu, Y. Rivenson, A. Feizi, and A. Ozcan, “Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains,” J. Biophotonics 12(3), e201800335 (2019).
[Crossref]

Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light: Sci. Appl. 7(2), 17141 (2018).
[Crossref]

Thiebaut, E.

Tökés, S.

Z. Göröcs, L. Orzó, M. Kiss, V. Tóth, and S. Tökés, “In-line color digital holographic microscope for water quality measurements,” Proc. SPIE 7376, 737614 (2010).
[Crossref]

Torzynski, M.

Tóth, V.

Z. Göröcs, L. Orzó, M. Kiss, V. Tóth, and S. Tökés, “In-line color digital holographic microscope for water quality measurements,” Proc. SPIE 7376, 737614 (2010).
[Crossref]

Treanor, D.

E. L. Clarke and D. Treanor, “Colour in digital pathology: a review,” Histopathology 70(2), 153–163 (2017).
[Crossref]

W. C. Revie, M. Shires, P. Jackson, D. Brettle, R. Cochrane, and D. Treanor, “Color management in digital pathology,” in Analytical Cellular Pathology, vol. 2014 (Hindawi, 2014).

Trillo, C.

Á. F. Doval and C. Trillo, “Dimensionless formulation of the convolution and angular spectrum reconstruction methods in digital holography,” Proc. SPIE 7387, 73870U (2010).
[Crossref]

Tseng, D.

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref]

Tseng, D. K.

S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012).
[Crossref]

Ura, S.

P. Xia, Y. Ito, Y. Shimozato, T. Tahara, T. Kakue, Y. Awatsuji, K. Nishio, S. Ura, T. Kubota, and O. Matoba, “Digital holography using spectral estimation technique,” J. Disp. Technol. 10(3), 235–242 (2014).

Usson, Y.

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017).
[Crossref]

Vanmeerbeeck, G.

A. Yurt, R. Stahl, G. Vanmeerbeeck, Z. Lin, M. Jayapala, and A. Lambrechts, “Towards practical cost-effective lens-free imaging,” Proc. SPIE 10055, 100550J (2017).
[Crossref]

E. Mathieu, C. D. Paul, R. Stahl, G. Vanmeerbeeck, V. Reumers, C. Liu, K. Konstantopoulos, and L. Lagae, “Time-lapse lens-free imaging of cell migration in diverse physical microenvironments,” Lab Chip 16(17), 3304–3316 (2016).
[Crossref]

D. Vercruysse, A. Dusa, R. Stahl, G. Vanmeerbeeck, K. de Wijs, C. Liu, D. Prodanov, P. Peumans, and L. Lagae, “Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer,” Lab Chip 15(4), 1123–1132 (2015).
[Crossref]

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
[Crossref]

Vercruysse, D.

D. Vercruysse, A. Dusa, R. Stahl, G. Vanmeerbeeck, K. de Wijs, C. Liu, D. Prodanov, P. Peumans, and L. Lagae, “Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer,” Lab Chip 15(4), 1123–1132 (2015).
[Crossref]

Verrier, N.

Vincent, R.

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017).
[Crossref]

Vollmer, A.

X. Mo, B. Kemper, P. Langehanenberg, A. Vollmer, J. Xie, and G. von Bally, “Application of color digital holographic microscopy for analysis of stained tissue sections,” in European Conference on Biomedical Optics, (Optical Society of America, 2009). Paper 7367_18.

von Bally, G.

X. Mo, B. Kemper, P. Langehanenberg, A. Vollmer, J. Xie, and G. von Bally, “Application of color digital holographic microscopy for analysis of stained tissue sections,” in European Conference on Biomedical Optics, (Optical Society of America, 2009). Paper 7367_18.

Vukicevic, D.

Wang, D.

L. Rong, Y. Li, S. Liu, W. Xiao, F. Pan, and D. Wang, “Iterative solution to twin image problem in in-line digital holography,” Opt. Lasers Eng. 51(5), 553–559 (2013).
[Crossref]

Wang, H.

Y. Rivenson, Y. Wu, H. Wang, Y. Zhang, A. Feizi, and A. Ozcan, “Sparsity-based multi-height phase recovery in holographic microscopy,” Sci. Rep. 6(1), 37862 (2016).
[Crossref]

Wang, Z.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
[Crossref]

Wei, Z.

T. Liu, Z. Wei, Y. Rivenson, K. de Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019).
[Crossref]

Weissleder, R.

J. Song, C. L. Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6(1), 24681 (2016).
[Crossref]

Wilts, B. D.

D. G. Stavenga, H. L. Leertouwer, and B. D. Wilts, “Quantifying the refractive index dispersion of a pigmented biological tissue using jamin–lebedeff interference microscopy,” Light: Sci. Appl. 2(9), e100 (2013).
[Crossref]

Wu, Y.

Y. Zhang, T. Liu, Y. Huang, D. Teng, Y. Bian, Y. Wu, Y. Rivenson, A. Feizi, and A. Ozcan, “Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains,” J. Biophotonics 12(3), e201800335 (2019).
[Crossref]

T. Liu, Z. Wei, Y. Rivenson, K. de Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019).
[Crossref]

Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. De Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3d imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25–27 (2019).
[Crossref]

Y. Rivenson, Y. Wu, and A. Ozcan, “Deep learning in holography and coherent imaging,” Light: Sci. Appl. 8(1), 85–88 (2019).
[Crossref]

Y. Rivenson, Y. Wu, H. Wang, Y. Zhang, A. Feizi, and A. Ozcan, “Sparsity-based multi-height phase recovery in holographic microscopy,” Sci. Rep. 6(1), 37862 (2016).
[Crossref]

Xia, P.

P. Xia, Y. Ito, Y. Shimozato, T. Tahara, T. Kakue, Y. Awatsuji, K. Nishio, S. Ura, T. Kubota, and O. Matoba, “Digital holography using spectral estimation technique,” J. Disp. Technol. 10(3), 235–242 (2014).

Xiao, W.

L. Rong, Y. Li, S. Liu, W. Xiao, F. Pan, and D. Wang, “Iterative solution to twin image problem in in-line digital holography,” Opt. Lasers Eng. 51(5), 553–559 (2013).
[Crossref]

Xie, J.

X. Mo, B. Kemper, P. Langehanenberg, A. Vollmer, J. Xie, and G. von Bally, “Application of color digital holographic microscopy for analysis of stained tissue sections,” in European Conference on Biomedical Optics, (Optical Society of America, 2009). Paper 7367_18.

Xu, W.

Y. Garcia, F. P. N.

S. V. Kesavan, F. P. N. Y. Garcia, M. Menneteau, F. Mittler, B. David-Watine, N. Dubrulle, S. L. Shorte, B. Chalmond, J.-M. Dinten, and C. P. Allier, “Real-time label-free detection of dividing cells by means of lensfree video-microscopy,” J. Biomed. Opt. 19(3), 1 (2014).
[Crossref]

Yagi, Y.

P. A. Bautista, N. Hashimoto, and Y. Yagi, “Color standardization in whole slide imaging using a color calibration slide,” J. Pathol. Inform. 5(1), 4 (2014).
[Crossref]

Yaglidere, O.

S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012).
[Crossref]

Yang, S.

Yaqoob, Z.

Y. Sung, N. Lue, B. Hamza, J. Martel, D. Irimia, R. R. Dasari, W. Choi, Z. Yaqoob, and P. So, “Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel,” Phys. Rev. Appl. 1(1), 014002 (2014).
[Crossref]

Yashiro, M.

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
[Crossref]

Yatagai, T.

Yu, L.

M. K. Kim, L. Yu, and C. J. Mann, “Interference techniques in digital holography,” J. Opt. A: Pure Appl. Opt. 8(7), S518–S523 (2006).
[Crossref]

Yu, N.

Y. Fang, N. Yu, Y. Jiang, and C. Dang, “High-precision lens-less flow cytometer on a chip,” Micromachines 9(5), 227 (2018).
[Crossref]

Yurt, A.

Z. Luo, A. Yurt, R. Stahl, A. Lambrechts, V. Reumers, D. Braeken, and L. Lagae, “Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks,” Opt. Express 27(10), 13581–13595 (2019).
[Crossref]

A. Yurt, R. Stahl, G. Vanmeerbeeck, Z. Lin, M. Jayapala, and A. Lambrechts, “Towards practical cost-effective lens-free imaging,” Proc. SPIE 10055, 100550J (2017).
[Crossref]

Zhang, F.

Zhang, Y.

T. Liu, Z. Wei, Y. Rivenson, K. de Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019).
[Crossref]

Y. Zhang, T. Liu, Y. Huang, D. Teng, Y. Bian, Y. Wu, Y. Rivenson, A. Feizi, and A. Ozcan, “Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains,” J. Biophotonics 12(3), e201800335 (2019).
[Crossref]

Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light: Sci. Appl. 7(2), 17141 (2018).
[Crossref]

Y. Rivenson, Y. Wu, H. Wang, Y. Zhang, A. Feizi, and A. Ozcan, “Sparsity-based multi-height phase recovery in holographic microscopy,” Sci. Rep. 6(1), 37862 (2016).
[Crossref]

Zheng, X.

Appl. Opt. (6)

Biomed. Opt. Express (1)

Cytometry Part A (1)

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J.-M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry Part A 91(5), 433–442 (2017).
[Crossref]

Histopathology (1)

E. L. Clarke and D. Treanor, “Colour in digital pathology: a review,” Histopathology 70(2), 153–163 (2017).
[Crossref]

IEEE J. Sel. Top. Quantum Electron. (1)

S. O. Isikman, W. Bishara, O. Mudanyali, I. Sencan, T.-W. Su, D. K. Tseng, O. Yaglidere, U. Sikora, and A. Ozcan, “Lensfree on-chip microscopy and tomography for biomedical applications,” IEEE J. Sel. Top. Quantum Electron. 18(3), 1059–1072 (2012).
[Crossref]

IEEE Trans. on Image Process. (1)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Process. 13(4), 600–612 (2004).
[Crossref]

J. Biomed. Opt. (1)

S. V. Kesavan, F. P. N. Y. Garcia, M. Menneteau, F. Mittler, B. David-Watine, N. Dubrulle, S. L. Shorte, B. Chalmond, J.-M. Dinten, and C. P. Allier, “Real-time label-free detection of dividing cells by means of lensfree video-microscopy,” J. Biomed. Opt. 19(3), 1 (2014).
[Crossref]

J. Biophotonics (2)

Y. Zhang, T. Liu, Y. Huang, D. Teng, Y. Bian, Y. Wu, Y. Rivenson, A. Feizi, and A. Ozcan, “Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains,” J. Biophotonics 12(3), e201800335 (2019).
[Crossref]

T. Liu, Z. Wei, Y. Rivenson, K. de Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019).
[Crossref]

J. Disp. Technol. (1)

P. Xia, Y. Ito, Y. Shimozato, T. Tahara, T. Kakue, Y. Awatsuji, K. Nishio, S. Ura, T. Kubota, and O. Matoba, “Digital holography using spectral estimation technique,” J. Disp. Technol. 10(3), 235–242 (2014).

J. Med. Imag. (1)

P. Shrestha and B. Hulsken, “Color accuracy and reproducibility in whole slide imaging scanners,” J. Med. Imag. 1(2), 027501 (2014).
[Crossref]

J. Opt. A: Pure Appl. Opt. (1)

M. K. Kim, L. Yu, and C. J. Mann, “Interference techniques in digital holography,” J. Opt. A: Pure Appl. Opt. 8(7), S518–S523 (2006).
[Crossref]

J. Pathol. Inform. (1)

P. A. Bautista, N. Hashimoto, and Y. Yagi, “Color standardization in whole slide imaging using a color calibration slide,” J. Pathol. Inform. 5(1), 4 (2014).
[Crossref]

Lab Chip (5)

S. O. Isikman, I. Sencan, O. Mudanyali, W. Bishara, C. Oztoprak, and A. Ozcan, “Color and monochrome lensless on-chip imaging of caenorhabditis elegans over a wide field-of-view,” Lab Chip 10(9), 1109–1112 (2010).
[Crossref]

A. Greenbaum, U. Sikora, and A. Ozcan, “Field-portable wide-field microscopy of dense samples using multi-height pixel super-resolution based lensfree imaging,” Lab Chip 12(7), 1242–1245 (2012).
[Crossref]

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref]

D. Vercruysse, A. Dusa, R. Stahl, G. Vanmeerbeeck, K. de Wijs, C. Liu, D. Prodanov, P. Peumans, and L. Lagae, “Three-part differential of unlabeled leukocytes with a compact lens-free imaging flow cytometer,” Lab Chip 15(4), 1123–1132 (2015).
[Crossref]

E. Mathieu, C. D. Paul, R. Stahl, G. Vanmeerbeeck, V. Reumers, C. Liu, K. Konstantopoulos, and L. Lagae, “Time-lapse lens-free imaging of cell migration in diverse physical microenvironments,” Lab Chip 16(17), 3304–3316 (2016).
[Crossref]

Light: Sci. Appl. (4)

Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light: Sci. Appl. 7(2), 17141 (2018).
[Crossref]

Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. De Haan, and A. Ozcan, “Bright-field holography: cross-modality deep learning enables snapshot 3d imaging with bright-field contrast using a single hologram,” Light: Sci. Appl. 8(1), 25–27 (2019).
[Crossref]

Y. Rivenson, Y. Wu, and A. Ozcan, “Deep learning in holography and coherent imaging,” Light: Sci. Appl. 8(1), 85–88 (2019).
[Crossref]

D. G. Stavenga, H. L. Leertouwer, and B. D. Wilts, “Quantifying the refractive index dispersion of a pigmented biological tissue using jamin–lebedeff interference microscopy,” Light: Sci. Appl. 2(9), e100 (2013).
[Crossref]

Meas. Sci. Technol. (1)

G. Coppola, P. Ferraro, M. Iodice, S. De Nicola, A. Finizio, and S. Grilli, “A digital holographic microscope for complete characterization of microelectromechanical systems,” Meas. Sci. Technol. 15(3), 529–539 (2004).
[Crossref]

Micromachines (1)

Y. Fang, N. Yu, Y. Jiang, and C. Dang, “High-precision lens-less flow cytometer on a chip,” Micromachines 9(5), 227 (2018).
[Crossref]

Nature (1)

D. Gabor, “A new microscopic principle,” Nature 161(4098), 777–778 (1948).
[Crossref]

Opt. Express (4)

Opt. Lasers Eng. (1)

L. Rong, Y. Li, S. Liu, W. Xiao, F. Pan, and D. Wang, “Iterative solution to twin image problem in in-line digital holography,” Opt. Lasers Eng. 51(5), 553–559 (2013).
[Crossref]

Opt. Lett. (4)

Phys. Rev. Appl. (1)

Y. Sung, N. Lue, B. Hamza, J. Martel, D. Irimia, R. R. Dasari, W. Choi, Z. Yaqoob, and P. So, “Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel,” Phys. Rev. Appl. 1(1), 014002 (2014).
[Crossref]

Phys. Rev. Lett. (1)

T. Latychevskaia and H.-W. Fink, “Solution to the twin image problem in holography,” Phys. Rev. Lett. 98(23), 233901 (2007).
[Crossref]

Proc. SPIE (5)

L. Denis, C. Fournier, T. Fournel, and C. Ducottet, “Twin-image noise reduction by phase retrieval in in-line digital holography,” Proc. SPIE 5914, 59140J (2005).
[Crossref]

Z. Göröcs, L. Orzó, M. Kiss, V. Tóth, and S. Tökés, “In-line color digital holographic microscope for water quality measurements,” Proc. SPIE 7376, 737614 (2010).
[Crossref]

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, T. Nagata, S. Gomi, K. Hatabayashi, Y. Oshima, S. Ozaki, N. Nishishita, and S. Kawamata, “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).
[Crossref]

A. Yurt, R. Stahl, G. Vanmeerbeeck, Z. Lin, M. Jayapala, and A. Lambrechts, “Towards practical cost-effective lens-free imaging,” Proc. SPIE 10055, 100550J (2017).
[Crossref]

Á. F. Doval and C. Trillo, “Dimensionless formulation of the convolution and angular spectrum reconstruction methods in digital holography,” Proc. SPIE 7387, 73870U (2010).
[Crossref]

Sci. Rep. (3)

J. Song, C. L. Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6(1), 24681 (2016).
[Crossref]

Y. Rivenson, Y. Wu, H. Wang, Y. Zhang, A. Feizi, and A. Ozcan, “Sparsity-based multi-height phase recovery in holographic microscopy,” Sci. Rep. 6(1), 37862 (2016).
[Crossref]

S. V. Kesavan, F. Momey, O. Cioni, B. David-Watine, N. Dubrulle, S. Shorte, E. Sulpice, D. Freida, B. Chalmond, J. Dinten, X. Gidrol, and C. Allier, “High-throughput monitoring of major cell functions by means of lensfree video microscopy,” Sci. Rep. 4(1), 5942 (2015).
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Other (7)

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Figures (9)

Fig. 1.
Fig. 1. Conventional color lens-free imaging approach using illumination at primary RGB wavelengths. (a) Typical configuration for in-line (color) lens-free imaging. The light source illuminates the sample sequentially with blue, green and red light. The resulting scattered object field and unscattered reference field propagate to the image sensor. (b) The interference pattern (hologram) is detected at the image sensor for each illumination wavelength. (c) Backwards propagation of the hologram results in a numerically reconstructed image of the object. (d) Combination of amplitude images obtained at blue, green and red wavelengths, gives a color image reconstruction of the object. The object in this image is a cross section of a lily anther.
Fig. 2.
Fig. 2. Color LFI simulations. (a) RGB image representation of the synthetic object in which three channels are linked to the transmittance of the object at a corresponding illumination wavelength. (b) Defined transmittance profile at the illumination wavelengths. (c) Captured hologram intensities at each illumination wavelength. (d) Estimated transmittance profiles after reconstruction. (e) Color image representation through combination of the transmittance profiles from (d). (f) Transmittance profile obtained from the cross-sections indicated in (b). Illumination wavelengths 450nm, 520nm and 630nm are indicated through the blue, green and red outline in (b)-(d).
Fig. 3.
Fig. 3. Multi-wavelength phase retrieval process. (a) Relative depths of the hologram planes. (b) Multi-wavelength phase retrieved amplitude images are placed in the RGB channel of a color reconstructed image. Mixing of the object distribution at different illumination wavelengths, results in erroneous image reconstructions. (c) Detailed flow for one iteration of the multi-wavelength phase retrieval. Complex holograms, (amplitude and phase) are propagated between the different relative hologram planes. At each plane, calculated amplitude, $A_i'$ is replaced with the amplitude captured by the image sensor, $A_i$, while phase is retained as is indicated by the color-code and arrows.
Fig. 4.
Fig. 4. High-SNR image reconstruction for color lens-free imaging. Starting from amplitude and phase information, retrieved using high-SNR holograms, a complex hologram is calculated. The calculated hologram amplitude is replaced with the detected hologram amplitude at that wavelength. A final image reconstruction results in improved image reconstruction quality and robust reconstruction of the object’s transmission properties due to adequate phase information in the hologram plane.
Fig. 5.
Fig. 5. Schematic drawing of experimental setup: 1) Four pig-tailed laser diodes coupled to an RGB coupler 2), resulting in single output fiber 3). A colored object 4) is placed directly between light source and image sensor 5).
Fig. 6.
Fig. 6. Comparison of three color lens-free imaging approaches. (a) Conventional combination of reconstructed holograms. (b) Color lens-free imaging using RGB holograms in the phase retrieval procedure. (c) Color lens-free imaging using high-SNR reconstruction. (d) Bright-field microscope reference images.
Fig. 7.
Fig. 7. Comparison of color LFI imaging techniques on Bielschowsky stained brain tissue. (a) Captured holograms. (b) Conventional CLFI approach showing twin-image color artefacts. (c) RGB multi-wavelength phase retrieval, indicating color distortion and reduced amount of image details. (d) High-SNR imaging technique showing increased amount of image details and accurate amplitude image reconstructions. (e) Brightfield microscope reference image.
Fig. 8.
Fig. 8. Comparison of RGB multi-wavelength phase retrieval and high-SNR imaging technique on imaging of several sample - stain combinations for imaging of pathology slides. Samples include: H&E stained liver tissue, trichrome stained bone tissue, H&E stained appendix tissue and Bielschowsky silver stained brain tissue. Reference images obtained with brightfield microscope are included.
Fig. 9.
Fig. 9. Comparison of multi-wavelength phase retrieval with two or three wavelengths. In yellow (a), the achieved contrast of USAF group 7 element 6 increased from $\sim 48\%$ to $\sim 91\%$ when comparing direct reconstruction (b) with multi-wavelength image reconstructions with two (c) and three (d) wavelengths. In (e)-(i), twin image removal is compared between reconstruction without phase retrieval (f), two-wavelength (g) and three-wavelength (h) phase retrieval. The insets in (f)-(h) show the reconstruction result. Graph (i) shows quantification of removal of twin image with increasing number of iterations between 2L and 3L phase retrieval.

Tables (1)

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Table 1. SSIM comparison of high-SNR phase retrieval with microscope image compared to RGB multi-wavelength phase retrieval.

Equations (7)

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O ( x , y , λ i ) = t ( x , y , λ i ) = A ( x , y ; λ i ) exp [ j φ ( x , y ; λ i ) ]
H i ( f x , f y , z ; λ i ) = exp ( 2 π j z λ i 1 ( λ i f x ) 2 ( λ i f y ) 2 )
O ( x , y , z ; λ i ) = F 1 [ F ( I det ( X , Y , 0 ; λ i ) × H i ( f x , f y , z ; λ i ) ] = A ( x , y , z ; λ i ) exp [ j φ ( x , y , z ; λ i ) ]
Δ φ i = 2 π Δ t λ i ( n obj n med )
Δ φ j λ i λ j Δ φ i
I ( x , y ) = S ( λ ) T ( x , y , λ ) R ( λ ) d λ
I ( x , y ) = S 0 T ( x , y , λ 0 ) R ( λ 0 )

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