Abstract

Poor motion estimation and subsequent registration are detrimental to super-resolution (SR). In this paper, we present a camera sampling method for achieving SR in concentric circular trajectory sampling (CCTS). Using this method, we can precisely control regular radial and angular shifts in CCTS. SR techniques can be subsequently applied ring by ring in radial and angular dimensions. Not only does the proposed camera sampling method eliminate the transient behavior and increases the sampling speed in CCTS, it also preserves the SR accuracy. Our experimental results demonstrate that our approach can accurately discriminate SR pixels from blurry images.

© 2016 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Concentric circular trajectory sampling for super-resolution and image mosaicing

Xian Du, Nigel Kojimoto, and Brian W. Anthony
J. Opt. Soc. Am. A 32(2) 293-304 (2015)

Concentric circle scanning system for large-area and high-precision imaging

Xian Du and Brian Anthony
Opt. Express 23(15) 20014-20029 (2015)

Registration for images in the presence of additive and multiplicative fixed-pattern noise

Colm Lynch and Nicholas Devaney
Appl. Opt. 57(8) 1824-1831 (2018)

References

  • View by:
  • |
  • |
  • |

  1. S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21–36 (2003).
    [Crossref]
  2. S. Borman and R. L. Stevenson, “Spatial resolution enhancement of low-resolution image sequences-a comprehensive review with directions for future research,” Lab. for Image and Signal Analysis, University of Notre Dame, Tech. Rep. (1998).
  3. D. Robinson and P. Milanfar, “Fundamental performance limits in image registration,” IEEE Trans. Image Process. 13(9), 1185–1199 (2004).
    [Crossref] [PubMed]
  4. P. Vandewalle, S. Susstrunk, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP J. Adv. Signal Process. 2006, 1–15 (2006).
    [Crossref]
  5. W. R. Crum, T. Hartkens, and D. L. G. Hill, “Non-rigid image registration: theory and practice,” Br. J. Radiol. 77(2), S140–S153 (2004).
    [Crossref] [PubMed]
  6. P. Vandewalle, S. Süsstrunk, and M. Vetterli, “Superresolution images reconstructed from aliased images,” Proc. SPIE 5150, 1398–1405 (2003).
  7. L. Lucchese and G. M. Cortelazzo, “A noise-robust frequency domain technique for estimating planar roto-translations,” IEEE Trans. Sig. Process. 48, 1769–1786 (2002).
    [PubMed]
  8. D. P. Capel, Image Mosaicing and Super-resolution (Springer Science & Business Media, 2004).
  9. D. Keren, S. Peleg, and R. Brada, “Image sequence enhancement using sub-pixel displacements,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 1988), pp. 742–746.
    [Crossref]
  10. H. Ur and D. Gross, “Improved resolution from subpixel shifted pictures,” Graph Model Im. Proc. 54, 181–186 (1992).
  11. L. Poletto and P. Nicolosi, “Enhancing the spatial resolution of a two-dimensional discrete array detector,” Opt. Eng. 38(10), 1748–1757 (1999).
    [Crossref]
  12. S. Prasad, “Digital superresolution and the generalized sampling theorem,” J. Opt. Soc. Am. A 24(2), 311–325 (2007).
    [Crossref] [PubMed]
  13. A. Papoulis, “Generalized sampling expansion,” IEEE T. Circuit Syst. 24(11), 652–654 (1977).
    [Crossref]
  14. J. L. Brown, “Multi-channel sampling of low-pass signals,” IEEE T. Circuit Syst. 28(2), 101–106 (1981).
    [Crossref]
  15. S. Bonchev and K. Alexiev, “Improving super-resolution image reconstruction by in-plane camera rotation,” in 13th Conference on Information Fusion (IEEE, 2010), pp. 1–7.
    [Crossref]
  16. X. Du, N. Kojimoto, and B. W. Anthony, “Concentric circular trajectory sampling for super-resolution and image mosaicing,” J. Opt. Soc. Am. A 32(2), 293–304 (2015).
    [Crossref] [PubMed]
  17. X. Du and B. Anthony, “Concentric circle scanning system for large-area and high-precision imaging,” Opt. Express 23(15), 20014–20029 (2015).
    [Crossref] [PubMed]
  18. A. Zomet, A. Rav-Acha, and S. Peleg, “Robust super-resolution,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2001), pp. 645–650.
  19. M. Irani and S. Peleg, “Improving resolution by image registration,” Graph. Models Image Proc. 53(3), 231–239 (1991).
    [Crossref]
  20. http://www.aig-imaging.com/ .
  21. E. R. Davies, Machine Vision: Theory, Algorithms, Practicalities (Morgan Kauffman Publishers, 2005).
  22. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
    [Crossref] [PubMed]
  23. P. Rasti, H. Demirel, and G. Anbarjafari, “Improved iterative back projection for video super-resolution,” In 22nd Signal Processing and Communications Applications Conference (IEEE, 2014), pp. 552–555.
    [Crossref]
  24. T. S. Huang and P. M. Narendra, “Image restoration by singular value decomposition,” Appl. Opt. 14(9), 2213–2216 (1975).
    [Crossref] [PubMed]
  25. P. C. Handsen, J. G. Nagy, and D. P. O’Leary, Deblurring Images: Matrices, Spectra, and Filtering (Society for Industrial and Applied Mathematic, 2006).
  26. K. Nasrollahi, S. Escalera, P. Rasti, G. Anbarjafari, X. Baro, H. J. Escalante, and T. B. Moeslund, “Deep learning based super-resolution for improved action recognition,” In International Conference on Image Processing Theory, Tools and Applications (IEEE, 2015), pp. 67–72.
    [Crossref]
  27. T. Peleg and M. Elad, “A statistical prediction model based on sparse representations for single image super-resolution,” IEEE Trans. Image Process. 23(6), 2569–2582 (2014).
    [Crossref] [PubMed]

2015 (2)

2014 (1)

T. Peleg and M. Elad, “A statistical prediction model based on sparse representations for single image super-resolution,” IEEE Trans. Image Process. 23(6), 2569–2582 (2014).
[Crossref] [PubMed]

2007 (1)

2006 (1)

P. Vandewalle, S. Susstrunk, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP J. Adv. Signal Process. 2006, 1–15 (2006).
[Crossref]

2004 (3)

W. R. Crum, T. Hartkens, and D. L. G. Hill, “Non-rigid image registration: theory and practice,” Br. J. Radiol. 77(2), S140–S153 (2004).
[Crossref] [PubMed]

D. Robinson and P. Milanfar, “Fundamental performance limits in image registration,” IEEE Trans. Image Process. 13(9), 1185–1199 (2004).
[Crossref] [PubMed]

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

2003 (2)

S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21–36 (2003).
[Crossref]

P. Vandewalle, S. Süsstrunk, and M. Vetterli, “Superresolution images reconstructed from aliased images,” Proc. SPIE 5150, 1398–1405 (2003).

2002 (1)

L. Lucchese and G. M. Cortelazzo, “A noise-robust frequency domain technique for estimating planar roto-translations,” IEEE Trans. Sig. Process. 48, 1769–1786 (2002).
[PubMed]

1999 (1)

L. Poletto and P. Nicolosi, “Enhancing the spatial resolution of a two-dimensional discrete array detector,” Opt. Eng. 38(10), 1748–1757 (1999).
[Crossref]

1992 (1)

H. Ur and D. Gross, “Improved resolution from subpixel shifted pictures,” Graph Model Im. Proc. 54, 181–186 (1992).

1991 (1)

M. Irani and S. Peleg, “Improving resolution by image registration,” Graph. Models Image Proc. 53(3), 231–239 (1991).
[Crossref]

1981 (1)

J. L. Brown, “Multi-channel sampling of low-pass signals,” IEEE T. Circuit Syst. 28(2), 101–106 (1981).
[Crossref]

1977 (1)

A. Papoulis, “Generalized sampling expansion,” IEEE T. Circuit Syst. 24(11), 652–654 (1977).
[Crossref]

1975 (1)

Alexiev, K.

S. Bonchev and K. Alexiev, “Improving super-resolution image reconstruction by in-plane camera rotation,” in 13th Conference on Information Fusion (IEEE, 2010), pp. 1–7.
[Crossref]

Anbarjafari, G.

P. Rasti, H. Demirel, and G. Anbarjafari, “Improved iterative back projection for video super-resolution,” In 22nd Signal Processing and Communications Applications Conference (IEEE, 2014), pp. 552–555.
[Crossref]

K. Nasrollahi, S. Escalera, P. Rasti, G. Anbarjafari, X. Baro, H. J. Escalante, and T. B. Moeslund, “Deep learning based super-resolution for improved action recognition,” In International Conference on Image Processing Theory, Tools and Applications (IEEE, 2015), pp. 67–72.
[Crossref]

Anthony, B.

Anthony, B. W.

Baro, X.

K. Nasrollahi, S. Escalera, P. Rasti, G. Anbarjafari, X. Baro, H. J. Escalante, and T. B. Moeslund, “Deep learning based super-resolution for improved action recognition,” In International Conference on Image Processing Theory, Tools and Applications (IEEE, 2015), pp. 67–72.
[Crossref]

Bonchev, S.

S. Bonchev and K. Alexiev, “Improving super-resolution image reconstruction by in-plane camera rotation,” in 13th Conference on Information Fusion (IEEE, 2010), pp. 1–7.
[Crossref]

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. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Brada, R.

D. Keren, S. Peleg, and R. Brada, “Image sequence enhancement using sub-pixel displacements,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 1988), pp. 742–746.
[Crossref]

Brown, J. L.

J. L. Brown, “Multi-channel sampling of low-pass signals,” IEEE T. Circuit Syst. 28(2), 101–106 (1981).
[Crossref]

Cortelazzo, G. M.

L. Lucchese and G. M. Cortelazzo, “A noise-robust frequency domain technique for estimating planar roto-translations,” IEEE Trans. Sig. Process. 48, 1769–1786 (2002).
[PubMed]

Crum, W. R.

W. R. Crum, T. Hartkens, and D. L. G. Hill, “Non-rigid image registration: theory and practice,” Br. J. Radiol. 77(2), S140–S153 (2004).
[Crossref] [PubMed]

Demirel, H.

P. Rasti, H. Demirel, and G. Anbarjafari, “Improved iterative back projection for video super-resolution,” In 22nd Signal Processing and Communications Applications Conference (IEEE, 2014), pp. 552–555.
[Crossref]

Du, X.

Elad, M.

T. Peleg and M. Elad, “A statistical prediction model based on sparse representations for single image super-resolution,” IEEE Trans. Image Process. 23(6), 2569–2582 (2014).
[Crossref] [PubMed]

Escalante, H. J.

K. Nasrollahi, S. Escalera, P. Rasti, G. Anbarjafari, X. Baro, H. J. Escalante, and T. B. Moeslund, “Deep learning based super-resolution for improved action recognition,” In International Conference on Image Processing Theory, Tools and Applications (IEEE, 2015), pp. 67–72.
[Crossref]

Escalera, S.

K. Nasrollahi, S. Escalera, P. Rasti, G. Anbarjafari, X. Baro, H. J. Escalante, and T. B. Moeslund, “Deep learning based super-resolution for improved action recognition,” In International Conference on Image Processing Theory, Tools and Applications (IEEE, 2015), pp. 67–72.
[Crossref]

Gross, D.

H. Ur and D. Gross, “Improved resolution from subpixel shifted pictures,” Graph Model Im. Proc. 54, 181–186 (1992).

Hartkens, T.

W. R. Crum, T. Hartkens, and D. L. G. Hill, “Non-rigid image registration: theory and practice,” Br. J. Radiol. 77(2), S140–S153 (2004).
[Crossref] [PubMed]

Hill, D. L. G.

W. R. Crum, T. Hartkens, and D. L. G. Hill, “Non-rigid image registration: theory and practice,” Br. J. Radiol. 77(2), S140–S153 (2004).
[Crossref] [PubMed]

Huang, T. S.

Irani, M.

M. Irani and S. Peleg, “Improving resolution by image registration,” Graph. Models Image Proc. 53(3), 231–239 (1991).
[Crossref]

Kang, M. G.

S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21–36 (2003).
[Crossref]

Keren, D.

D. Keren, S. Peleg, and R. Brada, “Image sequence enhancement using sub-pixel displacements,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 1988), pp. 742–746.
[Crossref]

Kojimoto, N.

Lucchese, L.

L. Lucchese and G. M. Cortelazzo, “A noise-robust frequency domain technique for estimating planar roto-translations,” IEEE Trans. Sig. Process. 48, 1769–1786 (2002).
[PubMed]

Milanfar, P.

D. Robinson and P. Milanfar, “Fundamental performance limits in image registration,” IEEE Trans. Image Process. 13(9), 1185–1199 (2004).
[Crossref] [PubMed]

Moeslund, T. B.

K. Nasrollahi, S. Escalera, P. Rasti, G. Anbarjafari, X. Baro, H. J. Escalante, and T. B. Moeslund, “Deep learning based super-resolution for improved action recognition,” In International Conference on Image Processing Theory, Tools and Applications (IEEE, 2015), pp. 67–72.
[Crossref]

Narendra, P. M.

Nasrollahi, K.

K. Nasrollahi, S. Escalera, P. Rasti, G. Anbarjafari, X. Baro, H. J. Escalante, and T. B. Moeslund, “Deep learning based super-resolution for improved action recognition,” In International Conference on Image Processing Theory, Tools and Applications (IEEE, 2015), pp. 67–72.
[Crossref]

Nicolosi, P.

L. Poletto and P. Nicolosi, “Enhancing the spatial resolution of a two-dimensional discrete array detector,” Opt. Eng. 38(10), 1748–1757 (1999).
[Crossref]

Papoulis, A.

A. Papoulis, “Generalized sampling expansion,” IEEE T. Circuit Syst. 24(11), 652–654 (1977).
[Crossref]

Park, M. K.

S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21–36 (2003).
[Crossref]

Park, S. C.

S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21–36 (2003).
[Crossref]

Peleg, S.

M. Irani and S. Peleg, “Improving resolution by image registration,” Graph. Models Image Proc. 53(3), 231–239 (1991).
[Crossref]

D. Keren, S. Peleg, and R. Brada, “Image sequence enhancement using sub-pixel displacements,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 1988), pp. 742–746.
[Crossref]

A. Zomet, A. Rav-Acha, and S. Peleg, “Robust super-resolution,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2001), pp. 645–650.

Peleg, T.

T. Peleg and M. Elad, “A statistical prediction model based on sparse representations for single image super-resolution,” IEEE Trans. Image Process. 23(6), 2569–2582 (2014).
[Crossref] [PubMed]

Poletto, L.

L. Poletto and P. Nicolosi, “Enhancing the spatial resolution of a two-dimensional discrete array detector,” Opt. Eng. 38(10), 1748–1757 (1999).
[Crossref]

Prasad, S.

Rasti, P.

K. Nasrollahi, S. Escalera, P. Rasti, G. Anbarjafari, X. Baro, H. J. Escalante, and T. B. Moeslund, “Deep learning based super-resolution for improved action recognition,” In International Conference on Image Processing Theory, Tools and Applications (IEEE, 2015), pp. 67–72.
[Crossref]

P. Rasti, H. Demirel, and G. Anbarjafari, “Improved iterative back projection for video super-resolution,” In 22nd Signal Processing and Communications Applications Conference (IEEE, 2014), pp. 552–555.
[Crossref]

Rav-Acha, A.

A. Zomet, A. Rav-Acha, and S. Peleg, “Robust super-resolution,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2001), pp. 645–650.

Robinson, D.

D. Robinson and P. Milanfar, “Fundamental performance limits in image registration,” IEEE Trans. Image Process. 13(9), 1185–1199 (2004).
[Crossref] [PubMed]

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. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

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. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Susstrunk, S.

P. Vandewalle, S. Susstrunk, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP J. Adv. Signal Process. 2006, 1–15 (2006).
[Crossref]

Süsstrunk, S.

P. Vandewalle, S. Süsstrunk, and M. Vetterli, “Superresolution images reconstructed from aliased images,” Proc. SPIE 5150, 1398–1405 (2003).

Ur, H.

H. Ur and D. Gross, “Improved resolution from subpixel shifted pictures,” Graph Model Im. Proc. 54, 181–186 (1992).

Vandewalle, P.

P. Vandewalle, S. Susstrunk, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP J. Adv. Signal Process. 2006, 1–15 (2006).
[Crossref]

P. Vandewalle, S. Süsstrunk, and M. Vetterli, “Superresolution images reconstructed from aliased images,” Proc. SPIE 5150, 1398–1405 (2003).

Vetterli, M.

P. Vandewalle, S. Susstrunk, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP J. Adv. Signal Process. 2006, 1–15 (2006).
[Crossref]

P. Vandewalle, S. Süsstrunk, and M. Vetterli, “Superresolution images reconstructed from aliased images,” Proc. SPIE 5150, 1398–1405 (2003).

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. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Zomet, A.

A. Zomet, A. Rav-Acha, and S. Peleg, “Robust super-resolution,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2001), pp. 645–650.

Appl. Opt. (1)

Br. J. Radiol. (1)

W. R. Crum, T. Hartkens, and D. L. G. Hill, “Non-rigid image registration: theory and practice,” Br. J. Radiol. 77(2), S140–S153 (2004).
[Crossref] [PubMed]

EURASIP J. Adv. Signal Process. (1)

P. Vandewalle, S. Susstrunk, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP J. Adv. Signal Process. 2006, 1–15 (2006).
[Crossref]

Graph Model Im. Proc. (1)

H. Ur and D. Gross, “Improved resolution from subpixel shifted pictures,” Graph Model Im. Proc. 54, 181–186 (1992).

Graph. Models Image Proc. (1)

M. Irani and S. Peleg, “Improving resolution by image registration,” Graph. Models Image Proc. 53(3), 231–239 (1991).
[Crossref]

IEEE Signal Process. Mag. (1)

S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: a technical overview,” IEEE Signal Process. Mag. 20(3), 21–36 (2003).
[Crossref]

IEEE T. Circuit Syst. (2)

A. Papoulis, “Generalized sampling expansion,” IEEE T. Circuit Syst. 24(11), 652–654 (1977).
[Crossref]

J. L. Brown, “Multi-channel sampling of low-pass signals,” IEEE T. Circuit Syst. 28(2), 101–106 (1981).
[Crossref]

IEEE Trans. Image Process. (3)

D. Robinson and P. Milanfar, “Fundamental performance limits in image registration,” IEEE Trans. Image Process. 13(9), 1185–1199 (2004).
[Crossref] [PubMed]

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

T. Peleg and M. Elad, “A statistical prediction model based on sparse representations for single image super-resolution,” IEEE Trans. Image Process. 23(6), 2569–2582 (2014).
[Crossref] [PubMed]

IEEE Trans. Sig. Process. (1)

L. Lucchese and G. M. Cortelazzo, “A noise-robust frequency domain technique for estimating planar roto-translations,” IEEE Trans. Sig. Process. 48, 1769–1786 (2002).
[PubMed]

J. Opt. Soc. Am. A (2)

Opt. Eng. (1)

L. Poletto and P. Nicolosi, “Enhancing the spatial resolution of a two-dimensional discrete array detector,” Opt. Eng. 38(10), 1748–1757 (1999).
[Crossref]

Opt. Express (1)

Proc. SPIE (1)

P. Vandewalle, S. Süsstrunk, and M. Vetterli, “Superresolution images reconstructed from aliased images,” Proc. SPIE 5150, 1398–1405 (2003).

Other (10)

D. P. Capel, Image Mosaicing and Super-resolution (Springer Science & Business Media, 2004).

D. Keren, S. Peleg, and R. Brada, “Image sequence enhancement using sub-pixel displacements,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 1988), pp. 742–746.
[Crossref]

S. Borman and R. L. Stevenson, “Spatial resolution enhancement of low-resolution image sequences-a comprehensive review with directions for future research,” Lab. for Image and Signal Analysis, University of Notre Dame, Tech. Rep. (1998).

A. Zomet, A. Rav-Acha, and S. Peleg, “Robust super-resolution,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2001), pp. 645–650.

http://www.aig-imaging.com/ .

E. R. Davies, Machine Vision: Theory, Algorithms, Practicalities (Morgan Kauffman Publishers, 2005).

S. Bonchev and K. Alexiev, “Improving super-resolution image reconstruction by in-plane camera rotation,” in 13th Conference on Information Fusion (IEEE, 2010), pp. 1–7.
[Crossref]

P. Rasti, H. Demirel, and G. Anbarjafari, “Improved iterative back projection for video super-resolution,” In 22nd Signal Processing and Communications Applications Conference (IEEE, 2014), pp. 552–555.
[Crossref]

P. C. Handsen, J. G. Nagy, and D. P. O’Leary, Deblurring Images: Matrices, Spectra, and Filtering (Society for Industrial and Applied Mathematic, 2006).

K. Nasrollahi, S. Escalera, P. Rasti, G. Anbarjafari, X. Baro, H. J. Escalante, and T. B. Moeslund, “Deep learning based super-resolution for improved action recognition,” In International Conference on Image Processing Theory, Tools and Applications (IEEE, 2015), pp. 67–72.
[Crossref]

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (9)

Fig. 1
Fig. 1 CCTS sampling position. (a) Bottom right quadrant optimized CCTS with sampling position (blue dot), pixel coverage (cyan rectangle) and ideal pixel shape (blue polygon). (b) Ring-based CCTS. (c) HR sector basis of ring-based CCTS for SR.
Fig. 2
Fig. 2 Registration of camera pixels in rotational system. (a) Camera-array resolution and camera-array pixels in rotational sampling system. (b-c) Matching of camera sampling and ring sampling.
Fig. 3
Fig. 3 The determination of column number of camera array for camera sampling (e.g. Max column # for 16 ring is: 4.) (a) maximum column numbers determined respectively by Eqs. (11-14) for rings 1-16 (highlight of the small range in rectangle of (b). (b) maximum column numbers for rings 1-100.
Fig. 4
Fig. 4 Vibration of camera sampling and ring-based CCTS: row 1 – acceleration time series along X axis; row 2 – acceleration time series along Y axis; row 3 – spectra of acceleration along Y axis; column 1 – Camera sampling with concentric CAV circular scan; column 2 – Ring-based sampling with concentric CAV circular scan.
Fig. 5
Fig. 5 Positioning error at the start of each ring for ring-based and camera sampling. (a) The image of a line of spots acquired by scans. (b) Positioning error along X axis. (c) Positioning error along Y axis. ‘o’ and ‘*’ are respectively the positioning errors of the start of each ring in ring-based and camera sampling. Here each pixel scale is 11 μm.
Fig. 6
Fig. 6 ISO_12233 target: LR and SR results of CCTS ring-based sampling and camera sampling with the evaluation of Fourier spectrum and “oriented energy”: Row 1 – Ground truth (400 × 400); Row 2 – One blurred and noisy LR image; Row 3 – POCS SR result (400 × 400) of ring-based sampling; Row 4 – POCS SR result (400 × 400) of camera sampling; Column 1– Images; Column 2– Fourier spectrum; Column 3 – Strength of the dominant energy; Column 4 – Direction of dominant “oriented energy”. Note the frequency and energy analysis of ground truth was only performed in the same round area as those of the SR results.
Fig. 7
Fig. 7 SR results of ring-based and camera sampling of synthetic images with the evaluation of “oriented energy”: row 1 – images of bridge; row 2 – direction of dominant “oriented energy” of row 1; row3 – images of houses; row 4 – direction of dominant “oriented energy” of row 3; column 1 – ground truth; column 2 – LR image; column 3 – SR for ring-based sampling; column 4 – SR for camera sampling.
Fig. 8
Fig. 8 Performance of SR results of sampling of synthetic images: (a) RMSE; (b) SSIM.
Fig. 9
Fig. 9 SR results of ring-based and camera sampling of target QA30. (a) SR for camera sampling. (b) SR for ring-based sampling. (c) 20 × image of the target. (d) LR image of CCTS sampling.

Tables (1)

Tables Icon

Table 1 Comparison of ring−based sampling and camera sampling methods for M × M SR (M is the number of regular shifts in radial direction and angular direction).

Equations (28)

Equations on this page are rendered with MathJax. Learn more.

N l ^ =[ 2π Ρ l ],
Δ Φ l = 2π N l ^ .
Ρ l =l· w l ·dX,
g( R,ϕ )= RΔR/2 R+ΔR/2 ϕΔϕ/2 ϕ+Δϕ/2 b( Rρ,ϕθ )·I( ρ,θ )dθ dρ+n( R,ϕ ),
g( R,ϕ )= ξ=Rm·Δρ/2 R+m·Δρ/2 η=ϕn·Δθ/2 ϕ+n·Δθ/2 B( ( R,ϕ )( ξ,η ) )·H( ξ,η ) +n( R,ϕ ),
H(R,ϕ) ^ =argmin( l g l ( R,ϕ ) g l ( R,ϕ ) ^ ),
r i,j = ( r 1,1 +( i1 )dX ) 2 + ( ( j1 )dX ) 2 ,
α i,j = α 1,1 tan 1 ( ( ( j1 )dX )/( r 1,1 +( i1 )dX ) ).
E r ( i,j )= [ l·M+( i1 ) ] 2 + [ ( j1 ) ] 2 l·M+( i1 ) 1,
E α ( i,j )= l·M· tan 1 [ j1 l·M+( i1 ) ] j1 1,
E r ( i,j )< β r ,
E α ( i,j )< β α .
| 1 f r (l,i,j) |< β Δr ,
| f α (l,i,j) |< β Δα .
E x j = 1 n1 i | Δ X i,j u xi | 2 ,
E y j = 1 n1 i | Δ Y i,j u yi | 2 ,
r l =l·M·dX,
Δ α l = N l*M ,
d( r i,j )= { 1+ [ ( j1 )dX r+( i1 )dX ] 2 } 1/2 dr,
d( α i,j )=dα+ ( j1 ) [ r+( i1 ) ] 2 + ( j1 ) 2 · dr dX .
d( r i,j )= f r (l,i,j)dr,
d( α i,j )=d α l + f α ( l,i,j )·d α l .
1 M ·[ ( j1 ) 1/ (1 β r ) 2 1 ( i1 ) ]<l.
d α l = 1 l· M 2 ·dX .
( j1 )l· M 2 ·dr ( l·M+i1 ) 2 + ( j1 ) 2 < β Δα .
r i,j = r 1,1 +( i1 )H,
α i,j = α 1,1 [ ( j1 )W ]/[ r 1,1 +( i1 )H ].
α i,j α 1,1 ( j1 )W/ r 1,1 .

Metrics