Abstract

Improving the spatial resolution of Optical Coherence Tomography (OCT) images is important for the visualization and analysis of small morphological features in biological tissue such as blood vessels, membranes, cellular layers, etc. In this paper, we propose a novel reconstruction approach to obtaining super-resolved OCT tomograms from multiple lower resolution images. The proposed Multi-Penalty Conditional Random Field (MPCRF) method combines four different penalty factors (spatial proximity, first and second order intensity variations, as well as a spline-based smoothness of fit) into the prior model within a Maximum A Posteriori (MAP) estimation framework. Test carried out in retinal OCT images illustrate the effectiveness of the proposed MPCRF reconstruction approach in terms of spatial resolution enhancement, as compared to previously published super resolved image reconstruction methods. Visual assessment of the MPCRF results demonstrate the potential of this method in better preservation of fine details and structures of the imaged sample, as well as retaining the sharpness of biological tissue boundaries while reducing the effects of speckle noise inherent to OCT. Quantitative evaluation using imaging metrics such as Signal-to-Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Equivalent Number of Looks (ENL), and Edge Preservation Parameter show significant visual quality improvement with the MPCRF approach. Therefore, the proposed MPCRF reconstruction approach is an effective tool for enhancing the spatial resolution of OCT images without the necessity for significant imaging hardware modifications.

© 2013 OSA

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2013 (6)

Z. Hu, X. Wu, Y. Ouyang, Y. Ouyang, and S. R. Sadda, “Semiautomated segmentation of the choroid in spectral-domain optical coherence tomography volume scans,” IOVS54, 1722–1729 (2013).

D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” J Biomed. Opt18, 056003 (2013).
[CrossRef]

F. Luan and Y. Wu, “Application of rpca in optical coherence tomography for speckle noise reduction,” Laser Phys. Lett.10, 035603 (2013).
[CrossRef]

J. J. Liu, I. Grulkowski, M. F. Kraus, B. Potsaid, C. D. Lu, B. Baumann, J. S. Duker, J. Hornegger, and J. G. Fujimoto, “In vivo imaging of the rodent eye with swept source/fourier domain oct,” Biomed. Opt. Express4, 351–363 (2013).
[CrossRef] [PubMed]

D. Yin, Y. Gu, and P. Xue, “Speckle-constrained variational methods for image restoration in optical coherence tomography,” J. Opt. Soc. Am. A30, 878–885 (2013).
[CrossRef]

M. Szkulmowski and M. Wojtkowski, “Averaging techniques for oct imaging,” Opt. Express21, 9757–9773 (2013).
[CrossRef] [PubMed]

2012 (6)

2010 (5)

2009 (3)

2008 (3)

2007 (3)

H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process.16, 349–366 (2007).
[CrossRef] [PubMed]

T. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration method and clinical examples,” J. Biomed. Opt.12, 41208–41208 (2007).
[CrossRef]

R. Zawadzki, A. Fuller, S. Choi, D. Wiley, B. Hamann, and J. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Proc. SPIE,” vol. 6426 (2007) pp. 642607.
[CrossRef]

2006 (2)

2005 (2)

S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation-based image restoration,” MMS.4, 460–489 (2005).

B. Sander, M. Larsen, L. Thrane, J. Hougaard, and T. Jørgensen, “Enhanced optical coherence tomography imaging by multiple scan averaging,” Br. J. Ophthalmol.89, 207–212 (2005).
[CrossRef] [PubMed]

2004 (2)

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Advances and challenges in super-resolution,” Int. J Imag. Syst. Tech.14, 47–57 (2004).
[CrossRef]

S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Trans. Image Process.13, 1327–1344 (2004).
[CrossRef] [PubMed]

2003 (2)

A. C. Akcay, J. P. Rolland, and J. M. Eichenholz, “Spectral shaping to improve the point spread function in optical coherence tomography,” Opt. Lett.28, 1921–1923 (2003).
[CrossRef] [PubMed]

A. Fercher, W. Drexler, C. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys.66, 239 (2003).
[CrossRef]

2002 (1)

W. T. Freeman, T. R. Jones, and E. C. Pasztor, “Example-based super-resolution,” IEEE Comput. Graph. Appl.22, 56–65 (2002).
[CrossRef]

2001 (1)

D. Rajan and S. Chaudhuri, “Generalized interpolation and its application in super-resolution imaging,” Imag. Vision Comput.19, 957–969 (2001).
[CrossRef]

2000 (1)

1999 (1)

J. Schmitt, S. Xiang, and K. Yung, “Speckle in optical coherence tomography,” Biomed. Opt.4, 95–105 (1999).
[CrossRef]

1991 (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

1984 (1)

R. Tsai and T. S. Huang, “Multiframe image restoration and registration,” A Comp. Vis. Image Process.1, 317–339 (1984).

1981 (1)

R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Trans. ASSP29, 1153–1160 (1981).
[CrossRef]

1956 (1)

P. Rosenbloom, “The method of steepest descent,” in “Proc. of Symp. in Applied Math,”, vol. 6 (1956), vol. 6, pp. 127–176.
[CrossRef]

Akcay, A. C.

Backman, V.

Bao, F.

D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” J Biomed. Opt18, 056003 (2013).
[CrossRef]

Bashkansky, M.

Baumann, B.

Bie, H.

Bizheva, K.

Bock, R.

Borman, S.

S. Borman and R. L. Stevenson, “Super-resolution from image sequences-a review,” in “Circuits and Systems, 1998. Proceedings. 1998 Midwest Symposium on,” (IEEE, 1998), pp. 374–378.

Bouma, B.

B. Bouma and Tearney, Handbook of optical coherence tomography (Marcel DekkerNew York:, 2002).

Boyd, S.

Bradu, A.

Brezinski, M.

M. Brezinski, Optical coherence tomography: principles and applications (Academic press, 2006).

Brzezinski, M.

Burger, M.

S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation-based image restoration,” MMS.4, 460–489 (2005).

Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

Chaudhuri, S.

D. Rajan and S. Chaudhuri, “Generalized interpolation and its application in super-resolution imaging,” Imag. Vision Comput.19, 957–969 (2001).
[CrossRef]

Cheng, K. H.

Chiu, S.

M. Robinson, S. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” in “Super-Resolution Imaging,” P. Milanfar, ed. (CRC Press, 2010), chap. 13, pp. 384–412.

Choi, S.

R. Zawadzki, A. Fuller, S. Choi, D. Wiley, B. Hamann, and J. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Proc. SPIE,” vol. 6426 (2007) pp. 642607.
[CrossRef]

Christensen, U.

T. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration method and clinical examples,” J. Biomed. Opt.12, 41208–41208 (2007).
[CrossRef]

Cigada, M.

A. Giani, M. Pellegrini, A. Invernizzi, M. Cigada, and G. Staurenghi, “Aligning scan locations from consecutive spectral-domain optical coherence tomography examinations: a comparison among different strategies,” IOVS53, 7637–7643 (2012).

Clausi, D. A.

Dainty, C.

Drexler, W.

A. Fercher, W. Drexler, C. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys.66, 239 (2003).
[CrossRef]

W. Drexler and J. G. Fujimoto, Optical coherence tomography: technology and applications (Springer, 2008).
[CrossRef]

Dubois, A.

Duker, J. S.

Eichenholz, J. M.

Elad, M.

S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Trans. Image Process.13, 1327–1344 (2004).
[CrossRef] [PubMed]

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Advances and challenges in super-resolution,” Int. J Imag. Syst. Tech.14, 47–57 (2004).
[CrossRef]

S. Farsiu, M. Elad, and P. Milanfar, “Constrained, globally optimal, multi-frame motion estimation,” in “Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on,” (IEEE, 2005), pp. 1396–1401.
[CrossRef]

Elsheikh, A.

D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” J Biomed. Opt18, 056003 (2013).
[CrossRef]

Fang, L.

Farsiu, S.

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express3, 927–942 (2012).
[CrossRef] [PubMed]

M. D. Robinson, C. A. Toth, J. Y. Lo, and S. Farsiu, “Efficient fourier-wavelet super-resolution,” IEEE Trans. Image Process.19, 2669–2681 (2010).
[CrossRef] [PubMed]

H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process.16, 349–366 (2007).
[CrossRef] [PubMed]

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Advances and challenges in super-resolution,” Int. J Imag. Syst. Tech.14, 47–57 (2004).
[CrossRef]

S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Trans. Image Process.13, 1327–1344 (2004).
[CrossRef] [PubMed]

M. Robinson, S. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” in “Super-Resolution Imaging,” P. Milanfar, ed. (CRC Press, 2010), chap. 13, pp. 384–412.

S. Farsiu, M. Elad, and P. Milanfar, “Constrained, globally optimal, multi-frame motion estimation,” in “Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on,” (IEEE, 2005), pp. 1396–1401.
[CrossRef]

Fercher, A.

A. Fercher, W. Drexler, C. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys.66, 239 (2003).
[CrossRef]

Ferguson, R. A.

Fieguth, P.

Fienup, J. R.

Flotte, T.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

Forbes, P.

Freeman, W. T.

W. T. Freeman, T. R. Jones, and E. C. Pasztor, “Example-based super-resolution,” IEEE Comput. Graph. Appl.22, 56–65 (2002).
[CrossRef]

Fujimoto, J. G.

Fuller, A.

R. Zawadzki, A. Fuller, S. Choi, D. Wiley, B. Hamann, and J. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Proc. SPIE,” vol. 6426 (2007) pp. 642607.
[CrossRef]

Georges, P.

Giani, A.

A. Giani, M. Pellegrini, A. Invernizzi, M. Cigada, and G. Staurenghi, “Aligning scan locations from consecutive spectral-domain optical coherence tomography examinations: a comparison among different strategies,” IOVS53, 7637–7643 (2012).

Goldfarb, D.

S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation-based image restoration,” MMS.4, 460–489 (2005).

Gong, J.

Gorczynska, I.

Gregory, K.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

Grimwood, A.

Grulkowski, I.

Gu, Y.

Guizar-Sicairos, M.

Hamann, B.

R. Zawadzki, A. Fuller, S. Choi, D. Wiley, B. Hamann, and J. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Proc. SPIE,” vol. 6426 (2007) pp. 642607.
[CrossRef]

Hamarneh, G.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intra-retinal layer segmentation in optical coherence tomography using an active contour approach,” MICCAI5762, 649–656 (2009).

Hart, C.

Hee, M. R.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

Hitzenberger, C.

A. Fercher, W. Drexler, C. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys.66, 239 (2003).
[CrossRef]

Hornegger, J.

Hougaard, J.

B. Sander, M. Larsen, L. Thrane, J. Hougaard, and T. Jørgensen, “Enhanced optical coherence tomography imaging by multiple scan averaging,” Br. J. Ophthalmol.89, 207–212 (2005).
[CrossRef] [PubMed]

Hu, Z.

Z. Hu, X. Wu, Y. Ouyang, Y. Ouyang, and S. R. Sadda, “Semiautomated segmentation of the choroid in spectral-domain optical coherence tomography volume scans,” IOVS54, 1722–1729 (2013).

Huang, D.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

Huang, T. S.

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process.19, 2861–2873 (2010).
[CrossRef]

R. Tsai and T. S. Huang, “Multiframe image restoration and registration,” A Comp. Vis. Image Process.1, 317–339 (1984).

Invernizzi, A.

A. Giani, M. Pellegrini, A. Invernizzi, M. Cigada, and G. Staurenghi, “Aligning scan locations from consecutive spectral-domain optical coherence tomography examinations: a comparison among different strategies,” IOVS53, 7637–7643 (2012).

Izatt, J.

M. Robinson, S. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” in “Super-Resolution Imaging,” P. Milanfar, ed. (CRC Press, 2010), chap. 13, pp. 384–412.

Izatt, J. A.

Jones, T. R.

W. T. Freeman, T. R. Jones, and E. C. Pasztor, “Example-based super-resolution,” IEEE Comput. Graph. Appl.22, 56–65 (2002).
[CrossRef]

Jørgensen, T.

T. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration method and clinical examples,” J. Biomed. Opt.12, 41208–41208 (2007).
[CrossRef]

B. Sander, M. Larsen, L. Thrane, J. Hougaard, and T. Jørgensen, “Enhanced optical coherence tomography imaging by multiple scan averaging,” Br. J. Ophthalmol.89, 207–212 (2005).
[CrossRef] [PubMed]

Kang, M.

S. Park, M. Park, and M. Kang, “Super-resolution image reconstruction: a technical overview,” Signal Process. Mag., IEEE20, 21–36 (2003).
[CrossRef]

Keys, R.

R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Trans. ASSP29, 1153–1160 (1981).
[CrossRef]

Kim, Y.

Kowalczyk, A.

Kraus, M. F.

Lam, E. Y.

Larsen, M.

B. Sander, M. Larsen, L. Thrane, J. Hougaard, and T. Jørgensen, “Enhanced optical coherence tomography imaging by multiple scan averaging,” Br. J. Ophthalmol.89, 207–212 (2005).
[CrossRef] [PubMed]

Lasser, T.

A. Fercher, W. Drexler, C. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys.66, 239 (2003).
[CrossRef]

Li, S.

Li, X.

Liang, Y.

Lin, C. P.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

Liu, B.

Liu, C.

Liu, J. J.

Liu, Y.

Lo, J.

M. Robinson, S. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” in “Super-Resolution Imaging,” P. Milanfar, ed. (CRC Press, 2010), chap. 13, pp. 384–412.

Lo, J. Y.

M. D. Robinson, C. A. Toth, J. Y. Lo, and S. Farsiu, “Efficient fourier-wavelet super-resolution,” IEEE Trans. Image Process.19, 2669–2681 (2010).
[CrossRef] [PubMed]

Lu, C. D.

Luan, F.

F. Luan and Y. Wu, “Application of rpca in optical coherence tomography for speckle noise reduction,” Laser Phys. Lett.10, 035603 (2013).
[CrossRef]

Ma, Y.

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process.19, 2861–2873 (2010).
[CrossRef]

Malchow, D.

Manduchi, R.

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in “Computer Vision, 1998. Sixth International Conference on,” (IEEE, 1998), pp. 839–846.

Mayer, M. A.

Merino, D.

Milanfar, P.

H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process.16, 349–366 (2007).
[CrossRef] [PubMed]

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Advances and challenges in super-resolution,” Int. J Imag. Syst. Tech.14, 47–57 (2004).
[CrossRef]

S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Trans. Image Process.13, 1327–1344 (2004).
[CrossRef] [PubMed]

S. Farsiu, M. Elad, and P. Milanfar, “Constrained, globally optimal, multi-frame motion estimation,” in “Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on,” (IEEE, 2005), pp. 1396–1401.
[CrossRef]

P. Milanfar, Super-resolution imaging (CRC Press, 2010).

Mishra, A.

Moreau, J.

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S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation-based image restoration,” MMS.4, 460–489 (2005).

Ouyang, Y.

Z. Hu, X. Wu, Y. Ouyang, Y. Ouyang, and S. R. Sadda, “Semiautomated segmentation of the choroid in spectral-domain optical coherence tomography volume scans,” IOVS54, 1722–1729 (2013).

Z. Hu, X. Wu, Y. Ouyang, Y. Ouyang, and S. R. Sadda, “Semiautomated segmentation of the choroid in spectral-domain optical coherence tomography volume scans,” IOVS54, 1722–1729 (2013).

Park, M.

S. Park, M. Park, and M. Kang, “Super-resolution image reconstruction: a technical overview,” Signal Process. Mag., IEEE20, 21–36 (2003).
[CrossRef]

Park, S.

S. Park, M. Park, and M. Kang, “Super-resolution image reconstruction: a technical overview,” Signal Process. Mag., IEEE20, 21–36 (2003).
[CrossRef]

Pasztor, E. C.

W. T. Freeman, T. R. Jones, and E. C. Pasztor, “Example-based super-resolution,” IEEE Comput. Graph. Appl.22, 56–65 (2002).
[CrossRef]

Pellegrini, M.

A. Giani, M. Pellegrini, A. Invernizzi, M. Cigada, and G. Staurenghi, “Aligning scan locations from consecutive spectral-domain optical coherence tomography examinations: a comparison among different strategies,” IOVS53, 7637–7643 (2012).

Podoleanu, A.

Potsaid, B.

Puliafito, C. A.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

Puvanathasan, P.

Rajan, D.

D. Rajan and S. Chaudhuri, “Generalized interpolation and its application in super-resolution imaging,” Imag. Vision Comput.19, 957–969 (2001).
[CrossRef]

Reintjes, J.

Ren, Z.

Robinson, D.

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Advances and challenges in super-resolution,” Int. J Imag. Syst. Tech.14, 47–57 (2004).
[CrossRef]

Robinson, M.

M. Robinson, S. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” in “Super-Resolution Imaging,” P. Milanfar, ed. (CRC Press, 2010), chap. 13, pp. 384–412.

Robinson, M. D.

M. D. Robinson, C. A. Toth, J. Y. Lo, and S. Farsiu, “Efficient fourier-wavelet super-resolution,” IEEE Trans. Image Process.19, 2669–2681 (2010).
[CrossRef] [PubMed]

S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Trans. Image Process.13, 1327–1344 (2004).
[CrossRef] [PubMed]

Rolland, J. P.

Rosenbloom, P.

P. Rosenbloom, “The method of steepest descent,” in “Proc. of Symp. in Applied Math,”, vol. 6 (1956), vol. 6, pp. 127–176.
[CrossRef]

Sacchet, D.

Sadda, S. R.

Z. Hu, X. Wu, Y. Ouyang, Y. Ouyang, and S. R. Sadda, “Semiautomated segmentation of the choroid in spectral-domain optical coherence tomography volume scans,” IOVS54, 1722–1729 (2013).

Sander, B.

T. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration method and clinical examples,” J. Biomed. Opt.12, 41208–41208 (2007).
[CrossRef]

B. Sander, M. Larsen, L. Thrane, J. Hougaard, and T. Jørgensen, “Enhanced optical coherence tomography imaging by multiple scan averaging,” Br. J. Ophthalmol.89, 207–212 (2005).
[CrossRef] [PubMed]

Sarunic, M.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intra-retinal layer segmentation in optical coherence tomography using an active contour approach,” MICCAI5762, 649–656 (2009).

Schmitt, J.

J. Schmitt, S. Xiang, and K. Yung, “Speckle in optical coherence tomography,” Biomed. Opt.4, 95–105 (1999).
[CrossRef]

Schuman, J. S.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

Smith, B.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intra-retinal layer segmentation in optical coherence tomography using an active contour approach,” MICCAI5762, 649–656 (2009).

Soliman, W.

T. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration method and clinical examples,” J. Biomed. Opt.12, 41208–41208 (2007).
[CrossRef]

Standish, B. A.

Staurenghi, G.

A. Giani, M. Pellegrini, A. Invernizzi, M. Cigada, and G. Staurenghi, “Aligning scan locations from consecutive spectral-domain optical coherence tomography examinations: a comparison among different strategies,” IOVS53, 7637–7643 (2012).

Stevenson, R. L.

S. Borman and R. L. Stevenson, “Super-resolution from image sequences-a review,” in “Circuits and Systems, 1998. Proceedings. 1998 Midwest Symposium on,” (IEEE, 1998), pp. 374–378.

Stinson, W. G.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

Swanson, E. A.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

Sylwestrzak, M.

Szkulmowski, M.

Szlag, D.

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H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process.16, 349–366 (2007).
[CrossRef] [PubMed]

Tearney,

B. Bouma and Tearney, Handbook of optical coherence tomography (Marcel DekkerNew York:, 2002).

Thomadsen, J.

T. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration method and clinical examples,” J. Biomed. Opt.12, 41208–41208 (2007).
[CrossRef]

Thrane, L.

B. Sander, M. Larsen, L. Thrane, J. Hougaard, and T. Jørgensen, “Enhanced optical coherence tomography imaging by multiple scan averaging,” Br. J. Ophthalmol.89, 207–212 (2005).
[CrossRef] [PubMed]

Thurman, S. T.

Tomasi, C.

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in “Computer Vision, 1998. Sixth International Conference on,” (IEEE, 1998), pp. 839–846.

Tomlins, P. H.

Toth, C.

M. Robinson, S. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” in “Super-Resolution Imaging,” P. Milanfar, ed. (CRC Press, 2010), chap. 13, pp. 384–412.

Toth, C. A.

L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, “Sparsity based denoising of spectral domain optical coherence tomography images,” Biomed. Opt. Express3, 927–942 (2012).
[CrossRef] [PubMed]

M. D. Robinson, C. A. Toth, J. Y. Lo, and S. Farsiu, “Efficient fourier-wavelet super-resolution,” IEEE Trans. Image Process.19, 2669–2681 (2010).
[CrossRef] [PubMed]

Tsai, R.

R. Tsai and T. S. Huang, “Multiframe image restoration and registration,” A Comp. Vis. Image Process.1, 317–339 (1984).

Wang, X.

X. Wang, “Method of steepest descent and its applications,” IEEE Microw. Wireless Compon. Lett.12, 24–26 (2008).

Werner, J.

R. Zawadzki, A. Fuller, S. Choi, D. Wiley, B. Hamann, and J. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Proc. SPIE,” vol. 6426 (2007) pp. 642607.
[CrossRef]

Wiley, D.

R. Zawadzki, A. Fuller, S. Choi, D. Wiley, B. Hamann, and J. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Proc. SPIE,” vol. 6426 (2007) pp. 642607.
[CrossRef]

Williams, D.

D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” J Biomed. Opt18, 056003 (2013).
[CrossRef]

Wojtkowski, M.

Wong, A.

Woolliams, P. D.

Wright, J.

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process.19, 2861–2873 (2010).
[CrossRef]

Wu, X.

Z. Hu, X. Wu, Y. Ouyang, Y. Ouyang, and S. R. Sadda, “Semiautomated segmentation of the choroid in spectral-domain optical coherence tomography volume scans,” IOVS54, 1722–1729 (2013).

Wu, Y.

F. Luan and Y. Wu, “Application of rpca in optical coherence tomography for speckle noise reduction,” Laser Phys. Lett.10, 035603 (2013).
[CrossRef]

Xiang, S.

J. Schmitt, S. Xiang, and K. Yung, “Speckle in optical coherence tomography,” Biomed. Opt.4, 95–105 (1999).
[CrossRef]

Xu, J.

S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation-based image restoration,” MMS.4, 460–489 (2005).

Xue, P.

Yang, J.

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process.19, 2861–2873 (2010).
[CrossRef]

Yang, V. X.

Yazdanpanah, A.

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intra-retinal layer segmentation in optical coherence tomography using an active contour approach,” MICCAI5762, 649–656 (2009).

Yin, D.

Yin, W.

S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation-based image restoration,” MMS.4, 460–489 (2005).

Yung, K.

J. Schmitt, S. Xiang, and K. Yung, “Speckle in optical coherence tomography,” Biomed. Opt.4, 95–105 (1999).
[CrossRef]

Zawadzki, R.

R. Zawadzki, A. Fuller, S. Choi, D. Wiley, B. Hamann, and J. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Proc. SPIE,” vol. 6426 (2007) pp. 642607.
[CrossRef]

Zheng, Y.

D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” J Biomed. Opt18, 056003 (2013).
[CrossRef]

Zhu, X.

A Comp. Vis. Image Process. (1)

R. Tsai and T. S. Huang, “Multiframe image restoration and registration,” A Comp. Vis. Image Process.1, 317–339 (1984).

Appl. Opt. (2)

Biomed. Opt. (1)

J. Schmitt, S. Xiang, and K. Yung, “Speckle in optical coherence tomography,” Biomed. Opt.4, 95–105 (1999).
[CrossRef]

Biomed. Opt. Express (3)

Br. J. Ophthalmol. (1)

B. Sander, M. Larsen, L. Thrane, J. Hougaard, and T. Jørgensen, “Enhanced optical coherence tomography imaging by multiple scan averaging,” Br. J. Ophthalmol.89, 207–212 (2005).
[CrossRef] [PubMed]

IEEE Comput. Graph. Appl. (1)

W. T. Freeman, T. R. Jones, and E. C. Pasztor, “Example-based super-resolution,” IEEE Comput. Graph. Appl.22, 56–65 (2002).
[CrossRef]

IEEE Microw. Wireless Compon. Lett. (1)

X. Wang, “Method of steepest descent and its applications,” IEEE Microw. Wireless Compon. Lett.12, 24–26 (2008).

IEEE Trans. ASSP (1)

R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Trans. ASSP29, 1153–1160 (1981).
[CrossRef]

IEEE Trans. Image Process. (4)

H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process.16, 349–366 (2007).
[CrossRef] [PubMed]

S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Trans. Image Process.13, 1327–1344 (2004).
[CrossRef] [PubMed]

J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process.19, 2861–2873 (2010).
[CrossRef]

M. D. Robinson, C. A. Toth, J. Y. Lo, and S. Farsiu, “Efficient fourier-wavelet super-resolution,” IEEE Trans. Image Process.19, 2669–2681 (2010).
[CrossRef] [PubMed]

Imag. Vision Comput. (1)

D. Rajan and S. Chaudhuri, “Generalized interpolation and its application in super-resolution imaging,” Imag. Vision Comput.19, 957–969 (2001).
[CrossRef]

Int. J Imag. Syst. Tech. (1)

S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Advances and challenges in super-resolution,” Int. J Imag. Syst. Tech.14, 47–57 (2004).
[CrossRef]

IOVS (2)

Z. Hu, X. Wu, Y. Ouyang, Y. Ouyang, and S. R. Sadda, “Semiautomated segmentation of the choroid in spectral-domain optical coherence tomography volume scans,” IOVS54, 1722–1729 (2013).

A. Giani, M. Pellegrini, A. Invernizzi, M. Cigada, and G. Staurenghi, “Aligning scan locations from consecutive spectral-domain optical coherence tomography examinations: a comparison among different strategies,” IOVS53, 7637–7643 (2012).

J Biomed. Opt (1)

D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” J Biomed. Opt18, 056003 (2013).
[CrossRef]

J. Biomed. Opt. (1)

T. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration method and clinical examples,” J. Biomed. Opt.12, 41208–41208 (2007).
[CrossRef]

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

Laser Phys. Lett. (1)

F. Luan and Y. Wu, “Application of rpca in optical coherence tomography for speckle noise reduction,” Laser Phys. Lett.10, 035603 (2013).
[CrossRef]

MICCAI (1)

A. Yazdanpanah, G. Hamarneh, B. Smith, and M. Sarunic, “Intra-retinal layer segmentation in optical coherence tomography using an active contour approach,” MICCAI5762, 649–656 (2009).

MMS. (1)

S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation-based image restoration,” MMS.4, 460–489 (2005).

Opt. Express (7)

Opt. Lett. (5)

Proc. of Symp. in Applied Math (1)

P. Rosenbloom, “The method of steepest descent,” in “Proc. of Symp. in Applied Math,”, vol. 6 (1956), vol. 6, pp. 127–176.
[CrossRef]

Proc. SPIE (1)

R. Zawadzki, A. Fuller, S. Choi, D. Wiley, B. Hamann, and J. Werner, “Correction of motion artifacts and scanning beam distortions in 3d ophthalmic optical coherence tomography imaging,” in “Proc. SPIE,” vol. 6426 (2007) pp. 642607.
[CrossRef]

Rep. Prog. Phys. (1)

A. Fercher, W. Drexler, C. Hitzenberger, and T. Lasser, “Optical coherence tomography-principles and applications,” Rep. Prog. Phys.66, 239 (2003).
[CrossRef]

Science (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and , “Optical coherence tomography,” Science254, 1178–1181 (1991).
[CrossRef] [PubMed]

Other (9)

W. Drexler and J. G. Fujimoto, Optical coherence tomography: technology and applications (Springer, 2008).
[CrossRef]

B. Bouma and Tearney, Handbook of optical coherence tomography (Marcel DekkerNew York:, 2002).

M. Brezinski, Optical coherence tomography: principles and applications (Academic press, 2006).

S. Borman and R. L. Stevenson, “Super-resolution from image sequences-a review,” in “Circuits and Systems, 1998. Proceedings. 1998 Midwest Symposium on,” (IEEE, 1998), pp. 374–378.

S. Park, M. Park, and M. Kang, “Super-resolution image reconstruction: a technical overview,” Signal Process. Mag., IEEE20, 21–36 (2003).
[CrossRef]

P. Milanfar, Super-resolution imaging (CRC Press, 2010).

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in “Computer Vision, 1998. Sixth International Conference on,” (IEEE, 1998), pp. 839–846.

M. Robinson, S. Chiu, J. Lo, C. Toth, J. Izatt, and S. Farsiu, “New applications of super-resolution in medical imaging,” in “Super-Resolution Imaging,” P. Milanfar, ed. (CRC Press, 2010), chap. 13, pp. 384–412.

S. Farsiu, M. Elad, and P. Milanfar, “Constrained, globally optimal, multi-frame motion estimation,” in “Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on,” (IEEE, 2005), pp. 1396–1401.
[CrossRef]

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Figures (14)

Fig. 1:
Fig. 1:

(a) High resolution human retina OCT image of size (512 × 512) pixels, (b) One sample LR OCT image of size (128 × 128) pixels generated from the reference OCT image in part (a).

Fig. 2:
Fig. 2:

(a) High resolution human retina OCT image of size (512 × 512) pixels, (b) One sample LR OCT image of size (226 × 226) pixels generated from the reference OCT image in part (a).

Fig. 3:
Fig. 3:

One sample rat retinal OCT image of size (324 × 1102) pixels.

Fig. 4:
Fig. 4:

(a) High resolution in-vivo human retial OCT image, (b) Simulated LR OCT image chosen, (c) Average of 5 registered LR simulated human retinal OCT images (d) Reconstructed SR OCT image using BI method from a single LR OCT image, (e–g) Samples of reconstructed SR OCT images using KBI, TV and MPCRF methods from a set of 5 LR OCT images, randomly chosen from a set of 16 LR human retinal OCT images. Blue, red and pink boxes in (b) specifies different areas around intra retinal layers, retinal tissue and blood vessel that are zoomed for the aim of better visualization as represented in Fig. 6(a–l).

Fig. 5:
Fig. 5:

(a) High resolution in-vivo rat retial OCT image, (b) Average of 3 registered rat retinal OCT images, (c) Reconstructed SR OCT image using BI method from the original high resolution rat retinal OCT image, (d–f) Samples of reconstructed SR OCT images using KBI, TV and MPCRF methods from a set of 3 LR OCT images, randomly chosen from a set of 512 high resolution rat retinal OCT images. Blue, red and pink boxes in (a) specifies different areas around intra retinal layers, retinal tissue and blood vessel that are zoomed for the aim of better visualization as represented in Fig. 7(a–l).

Fig. 6:
Fig. 6:

Specified enlarged regions in the LR OCT image of Fig. 4(b) and SR OCT images of Fig. 4(c–g), marked with blue as part of intra retinal layers, red as represents some morphological details of retina and pink as a region surrounding the blood vessel in Fig. 4(b). The first, second and third rows respectively represent those regions that are marked with blue, red and pink boxes.

Fig. 7:
Fig. 7:

Specified enlarged regions in the high resolution OCT image of Fig. 5(a) and SR OCT images of Fig. 5(b–f), marked with blue as part of intra retinal layers, red as represents some morphological details of retina and pink as a region surrounding the blood vessel in Fig. 5(a). The first, second and third rows respectively represent those regions that are marked with blue, red and pink boxes.

Fig. 8:
Fig. 8:

(a) Simulated LR human retina OCT image., (b) High resolution rat retina OCT image. Red boxes represent some homogenous regions of image used for obtaining the SNR, ENL and CNR metrics. The CNR in different areas is calculated by comparing the region inside of red box number 1 as a reference area and other regions that are specified using red boxes 2–6.

Fig. 9:
Fig. 9:

(a) High resolution human retina OCT image, (b) a sample of LR OCT image, (c) SR OCT image generated using BI method from a single LR OCT image, (d–f) Generated SR OCT images using KBI, TV and MPCRF methods from a set of 4 LR OCT images with undetermined spatial shifting among them.

Fig. 10:
Fig. 10:

(a) Generated SR OCT image using MPCRF method from a set of 5 LR simulated human retina OCT images, (b) Generated SR OCT image using MPCRF method form a set of 3 LR rat retina OCT images. Axial and lateral red lines encompass a fine details of represented OCT images (red circles) and are used to calculate the axial and lateral autocorrelation functions.

Fig. 11:
Fig. 11:

Axial ACF for SR OCT images of Fig. 4(d–g) using tested methods (BI, KBI, TV, MPCRF). Left column: ACF of two specified axial lines shown in Fig. 10(a). Right column: Normalized axial ACF.

Fig. 12:
Fig. 12:

Lateral ACF for the SR OCT images of Fig. 4(d–g) using tested methods (BI, KBI, TV, MPCRF). Left column: lateral ACF of two specified lateral lines shown in Fig. 10(a). Right column: Normalized lateral ACF.

Fig. 13:
Fig. 13:

Axial ACF for the SR OCT images of Fig. 5(c–f) using tested methods (BI, KBI, TV, MPCRF). Left column: axial ACF of two specified axial lines shown in Fig. 10(b). Right column: Normalized axial ACF.

Fig. 14:
Fig. 14:

Lateral ACF functions for the SR OCT images of Fig. 5(c–f) using tested methods (BI, KBI, TV, MPCRF). Left column: lateral ACF of two specified lateral lines shown in Fig. 10(b). Right column: Normalized lateral ACF.

Tables (2)

Tables Icon

Table 1: Image quality metrics evaluated for the generated SR image from a set of 5 LR simulated human retinal OCT acquisitions and with different methods: BI, KBI, TV, MPCRF. In the case of BI method, one LR simulated human retinal OCT image was used to generate the SR image. Values are relative to the original OCT image.

Tables Icon

Table 2: Image quality metrics evaluated for the generated SR image from a set of 3 LR rat retinal OCT acquisitions at the same location and with different methods: BI, KBI, TV, MPCRF. In the case of BI method, one LR rat retinal image was used to generate the SR image. Values are relative to the original OCT image.

Equations (16)

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log ( z k ) = log ( H k ( u ) ) + log ( η k ) ,
log ( z 1 ) = log ( H 1 ( u ) ) + log ( η 1 ) log ( z 2 ) = log ( H 2 ( u ) ) + log ( η 2 ) log ( z n ) = log ( H n ( u ) ) + log ( η n )
u ^ = argmax u ( P ( u | z 1 , z 2 , , z n ) ) ,
u ^ = argmax u ( P ( z 1 , z 2 , , z n | u ) P ( u ) ) ,
P ( z 1 , z 2 , z n | u ) = Π k Π i P ( z i k | u i ) = Π k Π i 1 σ 2 π exp ( α ( log ( z i k ) log ( H k ( u i ) ) ) 2 2 σ 2 )
P ( u ) = 1 f k i , k exp ( β | u i u j | w ( z i k , z j k ) ) ,
w S P ( i , j ) = exp ( d E ( i , j ) 2 σ S P 2 )
w F O V ( z i , z j ) = exp ( z i z j 2 σ F O V 2 )
w S O V ( z i , z j ) = exp ( D ( z i ) D ( z j ) 2 σ S O V 2 )
C ( u ) = exp ( γ D 2 S ( u ) )
P ( u ) = ( exp ( γ D 2 S ( u ) ) ) ( 1 f k i , j exp ( β | u i u j | w S P ( i , j ) w F O V ( z i k , z j k ) w S O V ( z i k , z j k ) ) ) ,
S N R = 1 R [ r = 1 R 10 log 10 ( μ r σ r 2 ) ] ,
C N R = 1 R [ r = 1 R μ r 1 μ r 2 σ r 1 2 + σ r 2 2 ) ] ,
E N L = 1 H [ h 1 h 1 μ h 2 σ h 2 ]
η = ( 2 V 2 V ¯ ) ( 2 G ^ 2 G ^ ¯ ) ( 2 V 2 V ¯ ) 2 ( 2 G ^ 2 G ^ ¯ ) 2
R X X = E [ X n + m X n ]

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