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[Crossref]

E. Soubies, F. Soulez, M. McCann, T.-a. Pham, L. Donati, T. Debarre, D. Sage, and M. Unser, “Pocket guide to solve inverse problems with Global Bio Im,” Inverse Probl. 35(10), 104006 (2019).

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M. Schürmann, J. Scholze, P. Müller, C. Chan, A. Ekpenyong, K. Chalut, and J. Guck, “Refractive index measurements of single spherical cells using digital holographic microscopy,” Methods Cell Biol. 125, 143–159 (2015).

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H. Qiao, J. Wu, X. Li, M. H. Shoreh, J. Fan, and Q. Dai, “GPU-based deep convolutional neural network for tomographic phase microscopy with l1 fitting and regularization,” J. Biomed. Opt. 23(06), 1 (2018).

[Crossref]

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[Crossref]

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[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach to optical tomography,” Optica 2(6), 517–522 (2015).

[Crossref]

M. Schürmann, J. Scholze, P. Müller, C. Chan, A. Ekpenyong, K. Chalut, and J. Guck, “Refractive index measurements of single spherical cells using digital holographic microscopy,” Methods Cell Biol. 125, 143–159 (2015).

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H. Gupta, K. H. Jin, H. Q. Nguyen, M. T. McCann, and M. Unser, “CNN-based projected gradient descent for consistent CT image reconstruction,” IEEE Transactions on Med. Imaging 37(6), 1440–1453 (2018).

[Crossref]

J. Schlemper, J. Caballero, J. V. Hajnal, A. Price, and D. Rueckert, “A deep cascade of convolutional neural networks for MR image reconstruction,” in International Conference on Information Processing in Medical Imaging (Springer, 2017), pp. 647–658.

K. Hammernik, T. Würfl, T. Pock, and A. Maier, “A deep learning architecture for limited-angle computed tomography reconstruction,” in Bildverarbeitung für die Medizin 2017 (Springer, 2017), pp. 92–97.

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in ICML Workshop on Deep Learning for Audio, Speech and Language Processing, vol. 28 (Citeseer, 2013).

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[Crossref]

H. K. Aggarwal, M. P. Mani, and M. Jacob, “MODL: Model-based deep learning architecture for inverse problems,” IEEE Transactions on Med. Imaging 38(2), 394–405 (2019).

[Crossref]

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U. S. Kamilov, H. Mansour, and B. Wohlberg, “A plug-and-play priors approach for solving nonlinear imaging inverse problems,” IEEE Signal Process. Lett. 24(12), 1872–1876 (2017).

[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Optical tomographic image reconstruction based on beam propagation and sparse regularization,” IEEE Transactions on Comput. Imaging 2(1), 59–70 (2016).

[Crossref]

U. S. Kamilov, D. Liu, H. Mansour, and P. T. Boufounos, “A recursive Born approach to nonlinear inverse scattering,” IEEE Signal Process. Lett. 23(8), 1052–1056 (2016).

[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach to optical tomography,” Optica 2(6), 517–522 (2015).

[Crossref]

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[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).

[Crossref]

Y. LeCun, K. Kavukcuoglu, and C. Farabet, “Convolutional networks and applications in vision,” in Proceedings of IEEE International Symposium on Circuits and Systems (IEEE, 2010), pp. 253–256.

P. Liu, L. Chin, W. Ser, H. Chen, C.-M. Hsieh, C.-H. Lee, K.-B. Sung, T. Ayi, P. Yap, B. Liedberg, and K. Wang, “Cell refractive index for cell biology and disease diagnosis: Past, present and future,” Lab Chip 16(4), 634–644 (2016).

[Crossref]

J. H. Rick Chang, C.-L. Li, B. Poczos, B. V. K. Vijaya Kumar, and A. C. Sankaranarayanan, “One network to solve them all – solving linear inverse problems using deep projection models,” in The IEEE International Conference on Computer Vision (ICCV), (2017), pp. 5888–5897.

L. Li, L. G. Wang, F. L. Teixeira, C. Liu, A. Nehorai, and T. J. Cui, “Deep NIS: Deep neural network for nonlinear electromagnetic inverse scattering,” IEEE Trans. Antennas Propag. 67(3), 1819–1825 (2019).

[Crossref]

H. Qiao, J. Wu, X. Li, M. H. Shoreh, J. Fan, and Q. Dai, “GPU-based deep convolutional neural network for tomographic phase microscopy with l1 fitting and regularization,” J. Biomed. Opt. 23(06), 1 (2018).

[Crossref]

P. Liu, L. Chin, W. Ser, H. Chen, C.-M. Hsieh, C.-H. Lee, K.-B. Sung, T. Ayi, P. Yap, B. Liedberg, and K. Wang, “Cell refractive index for cell biology and disease diagnosis: Past, present and future,” Lab Chip 16(4), 634–644 (2016).

[Crossref]

L. Li, L. G. Wang, F. L. Teixeira, C. Liu, A. Nehorai, and T. J. Cui, “Deep NIS: Deep neural network for nonlinear electromagnetic inverse scattering,” IEEE Trans. Antennas Propag. 67(3), 1819–1825 (2019).

[Crossref]

H.-Y. Liu, D. Liu, H. Mansour, P. T. Boufounos, L. Waller, and U. S. Kamilov, “SEAGLE: Sparsity-driven image reconstruction under multiple scattering,” IEEE Transactions on Comput. Imaging 4(1), 73–86 (2018).

[Crossref]

U. S. Kamilov, D. Liu, H. Mansour, and P. T. Boufounos, “A recursive Born approach to nonlinear inverse scattering,” IEEE Signal Process. Lett. 23(8), 1052–1056 (2016).

[Crossref]

H.-Y. Liu, D. Liu, H. Mansour, P. T. Boufounos, L. Waller, and U. S. Kamilov, “SEAGLE: Sparsity-driven image reconstruction under multiple scattering,” IEEE Transactions on Comput. Imaging 4(1), 73–86 (2018).

[Crossref]

P. Liu, L. Chin, W. Ser, H. Chen, C.-M. Hsieh, C.-H. Lee, K.-B. Sung, T. Ayi, P. Yap, B. Liedberg, and K. Wang, “Cell refractive index for cell biology and disease diagnosis: Past, present and future,” Lab Chip 16(4), 634–644 (2016).

[Crossref]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Transactions on Pattern Analysis Mach. Intell. 38(2), 295–307 (2016).

[Crossref]

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4(9), 717–719 (2007).

[Crossref]

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in ICML Workshop on Deep Learning for Audio, Speech and Language Processing, vol. 28 (Citeseer, 2013).

K. Hammernik, T. Würfl, T. Pock, and A. Maier, “A deep learning architecture for limited-angle computed tomography reconstruction,” in Bildverarbeitung für die Medizin 2017 (Springer, 2017), pp. 92–97.

H. K. Aggarwal, M. P. Mani, and M. Jacob, “MODL: Model-based deep learning architecture for inverse problems,” IEEE Transactions on Med. Imaging 38(2), 394–405 (2019).

[Crossref]

H.-Y. Liu, D. Liu, H. Mansour, P. T. Boufounos, L. Waller, and U. S. Kamilov, “SEAGLE: Sparsity-driven image reconstruction under multiple scattering,” IEEE Transactions on Comput. Imaging 4(1), 73–86 (2018).

[Crossref]

U. S. Kamilov, H. Mansour, and B. Wohlberg, “A plug-and-play priors approach for solving nonlinear imaging inverse problems,” IEEE Signal Process. Lett. 24(12), 1872–1876 (2017).

[Crossref]

U. S. Kamilov, D. Liu, H. Mansour, and P. T. Boufounos, “A recursive Born approach to nonlinear inverse scattering,” IEEE Signal Process. Lett. 23(8), 1052–1056 (2016).

[Crossref]

E. Soubies, F. Soulez, M. McCann, T.-a. Pham, L. Donati, T. Debarre, D. Sage, and M. Unser, “Pocket guide to solve inverse problems with Global Bio Im,” Inverse Probl. 35(10), 104006 (2019).

[Crossref]

H. Gupta, K. H. Jin, H. Q. Nguyen, M. T. McCann, and M. Unser, “CNN-based projected gradient descent for consistent CT image reconstruction,” IEEE Transactions on Med. Imaging 37(6), 1440–1453 (2018).

[Crossref]

M. T. McCann, K. H. Jin, and M. Unser, “Convolutional neural networks for inverse problems in imaging: A review,” IEEE Signal Process. Mag. 34(6), 85–95 (2017).

[Crossref]

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Transactions on Image Process. 26(9), 4509–4522 (2017).

[Crossref]

M. Schürmann, J. Scholze, P. Müller, C. Chan, A. Ekpenyong, K. Chalut, and J. Guck, “Refractive index measurements of single spherical cells using digital holographic microscopy,” Methods Cell Biol. 125, 143–159 (2015).

[Crossref]

T. Nguyen, V. Bui, and G. Nehmetallah, “3D optical diffraction tomography using deep learning,” in Digital Holography and Three-Dimensional Imaging (Optical Society of America, 2018), pp. DW2F–4.

L. Li, L. G. Wang, F. L. Teixeira, C. Liu, A. Nehorai, and T. J. Cui, “Deep NIS: Deep neural network for nonlinear electromagnetic inverse scattering,” IEEE Trans. Antennas Propag. 67(3), 1819–1825 (2019).

[Crossref]

A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” in ICML Workshop on Deep Learning for Audio, Speech and Language Processing, vol. 28 (Citeseer, 2013).

H. Gupta, K. H. Jin, H. Q. Nguyen, M. T. McCann, and M. Unser, “CNN-based projected gradient descent for consistent CT image reconstruction,” IEEE Transactions on Med. Imaging 37(6), 1440–1453 (2018).

[Crossref]

T. Nguyen, V. Bui, and G. Nehmetallah, “3D optical diffraction tomography using deep learning,” in Digital Holography and Three-Dimensional Imaging (Optical Society of America, 2018), pp. DW2F–4.

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4(9), 717–719 (2007).

[Crossref]

J. Adler and O. Öktem, “Solving ill-posed inverse problems using iterative deep neural networks,” Inverse Probl. 33(12), 124007 (2017).

[Crossref]

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60(1-4), 259–268 (1992).

[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Optical tomographic image reconstruction based on beam propagation and sparse regularization,” IEEE Transactions on Comput. Imaging 2(1), 59–70 (2016).

[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach to optical tomography,” Optica 2(6), 517–522 (2015).

[Crossref]

E. Soubies, F. Soulez, M. McCann, T.-a. Pham, L. Donati, T. Debarre, D. Sage, and M. Unser, “Pocket guide to solve inverse problems with Global Bio Im,” Inverse Probl. 35(10), 104006 (2019).

[Crossref]

E. Soubies, T.-a. Pham, and M. Unser, “Efficient inversion of multiple-scattering model for optical diffraction tomography,” Opt. Express 25(18), 21786–21800 (2017).

[Crossref]

K. Hammernik, T. Würfl, T. Pock, and A. Maier, “A deep learning architecture for limited-angle computed tomography reconstruction,” in Bildverarbeitung für die Medizin 2017 (Springer, 2017), pp. 92–97.

J. H. Rick Chang, C.-L. Li, B. Poczos, B. V. K. Vijaya Kumar, and A. C. Sankaranarayanan, “One network to solve them all – solving linear inverse problems using deep projection models,” in The IEEE International Conference on Computer Vision (ICCV), (2017), pp. 5888–5897.

J. Schlemper, J. Caballero, J. V. Hajnal, A. Price, and D. Rueckert, “A deep cascade of convolutional neural networks for MR image reconstruction,” in International Conference on Information Processing in Medical Imaging (Springer, 2017), pp. 647–658.

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Optical tomographic image reconstruction based on beam propagation and sparse regularization,” IEEE Transactions on Comput. Imaging 2(1), 59–70 (2016).

[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach to optical tomography,” Optica 2(6), 517–522 (2015).

[Crossref]

H. Qiao, J. Wu, X. Li, M. H. Shoreh, J. Fan, and Q. Dai, “GPU-based deep convolutional neural network for tomographic phase microscopy with l1 fitting and regularization,” J. Biomed. Opt. 23(06), 1 (2018).

[Crossref]

J. H. Rick Chang, C.-L. Li, B. Poczos, B. V. K. Vijaya Kumar, and A. C. Sankaranarayanan, “One network to solve them all – solving linear inverse problems using deep projection models,” in The IEEE International Conference on Computer Vision (ICCV), (2017), pp. 5888–5897.

H. Lantéri, M. Roche, O. Cuevas, and C. Aime, “A general method to devise maximum-likelihood signal restoration multiplicative algorithms with non-negativity constraints,” Signal Process. 81(5), 945–974 (2001).

[Crossref]

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2015), pp. 234–241.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D 60(1-4), 259–268 (1992).

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