Abstract

A number of despeckling methods for optical coherence tomography (OCT) have been proposed. In these digital filtering techniques, speckle noise is often simplified as additive white Gaussian noise due to the logarithmic compression for the signal. The approximation is not completely consistent with the characteristic of OCT speckle noise, and cannot be reasonably extended to deconvolution algorithms. This paper presents a deconvolution model that combines the variational regularization term with the statistical characteristic constraints of data corrupted by OCT speckle noise. In the data fidelity term, speckle noise is modeled as signal dependent, and the point spread function of OCT systems is included. The regularization functional introduces a priori information on the original images, and a regularization term based on block matching 3D modeling is used to construct the variational model in the paper. Finally, the method is applied to the restoration of actual OCT raw data of human skin. The numerical results demonstrate that the proposed deconvolution algorithm can simultaneously enhance regions of images containing detail and remove OCT speckle noise.

© 2013 Optical Society of America

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    [CrossRef]

2012

F. Dong, H. Zhang, and D. X. Kong, “Nonlocal total variation models for multiplicative noise removal using split Bregman iteration,” Math. Comput. Model. 55, 939–954(2012).
[CrossRef]

D. Q. Chen and L. Z. Cheng, “Spatially adapted regularization parameter selection based on the local discrepancy function for Poissonian image deblurring,” Inverse Probl. 28, 015004 (2012).
[CrossRef]

A. Danielyan, V. Katkovnik, and K. Egiazarian, “BM3D frames and variational image deblurring,” IEEE Trans. Image Process. 21, 1715–1728 (2012).
[CrossRef]

M. Szkulmowski, I. Gorczynska, D. Szlag, M. Sylwestrzak, A. Kowalczyk, and M. Wojtkowski, “Efficient reduction of speckle noise in optical coherence tomography,” Opt. Express 20, 1337–1359 (2012).
[CrossRef]

M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin, and R. P. Tornow, “Wavelet denoising of multiframe optical coherence tomography data,” Biomed. Opt. Express 3, 572–589 (2012).
[CrossRef]

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. Express 3, 927–942 (2012).
[CrossRef]

2011

K. Zhang and J. U. Kang, “Real-time intraoperative 4D full-range FD-OCT based on the dual graphics processing units architecture for microsurgery guidance,” Biomed. Opt. Express 2, 764–770 (2011).
[CrossRef]

S. Setzer, “Operator splittings, Bregman methods and frame shrinkage in image processing,” Int. J. Comput. Vis. 92, 265–280 (2011).
[CrossRef]

Z. Wang, H. Kyonob, H. G. Bezerrab, D. L. Wilson, M. A. Costa, and A. M. Rollins, “Automatic segmentation of intravascular optical coherence tomography images for facilitating quantitative diagnosis of atherosclerosis,” Proc. SPIE 7889, 78890N(2011).
[CrossRef]

R. Prashanth and S. Bhattacharya, “Space variant deconvolution for optical coherence tomography,” Proc. SPIE 8311, 831113 (2011).
[CrossRef]

A. Danielyan, V. Katkovnik, and K. Egiazarian, “Deblurring of Poissonian images using BM3D frames,” Proc. SPIE 8138, 813812 (2011).
[CrossRef]

D. A. Caneiro, S. A. Read, and M. J. Collins, “Speckle reduction in optical coherence tomography imaging by affine-motion image registration,” J. Biomed. Opt. 16, 116027 (2011).
[CrossRef]

2010

M. A. T. Figueiredo and J. M. Bioucas-Dias, “Restoration of Poissonian images using alternating direction optimization,” IEEE Trans. Image Process. 19, 3133–3145 (2010).
[CrossRef]

J. M. Bioucas-Dias and M. A. T. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Trans. Image Process. 19, 1720–1730 (2010).
[CrossRef]

E. Esser, X. Q. Zhang, and T. F. Chan, “A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science,” SIAM J. Imaging Sci. 3, 1015–1046 (2010).
[CrossRef]

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

Z. Jian, L. Yu, B. Rao, B. J. Tromberg, and Z. Chen, “Three-dimensional speckle suppression in optical coherence tomography based on the curvelet transform,” Opt. Express 18, 1024–1032 (2010).
[CrossRef]

R. Bernardes, C. Maduro, P. Serranho, A. Araujo, S. Barbeiro, and J. Cunha-Vaz, “Improved adaptive complex diffusion despeckling filter,” Opt. Express 18, 24048–24059 (2010).
[CrossRef]

2009

Y. Liu, Y. Liang, G. Mu, and X. Zhu, “Deconvolution methods for image deblurring in optical coherence tomography,” J. Opt. Soc. Am. A 26, 72–77 (2009).
[CrossRef]

P. Puvanathasan and K. Bizheva, “Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images,” Opt. Express 17, 733–746 (2009).
[CrossRef]

G. Gilboa and S. Osher, “Nonlocal operators with applications to image processing,” Multiscale Model. Simul. 7, 1005–1028 (2009).
[CrossRef]

L. Denis, F. Tupin, J. Darbon, and M. Sigelle, “SAR image regularization with fast approximate discrete minimization,” IEEE Trans. Image Process. 18, 1588–1600 (2009).
[CrossRef]

S. Chitchian, M. A. Fiddy, and N. M. Fried, “Denoising during optical coherence tomography of the prostate nerves via wavelet shrinkage using dual-tree complex wavelet transform,” J. Biomed. Opt. 14, 014031 (2009).
[CrossRef]

2008

J. Shi and S. Osher, “A nonlinear inverse scale space method for a convex multiplicative noise model,” SIAM J. Imaging Sci. 1, 294–321 (2008).
[CrossRef]

G. Aubert and J. Aujol, “A variational approach to remove multiplicative noise,” SIAM J. Appl. Math. 68, 925–946 (2008).
[CrossRef]

2007

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

H. M. Salinas and D. C. Fernandez, “Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography,” IEEE Trans. Med. Imaging 26, 761–771 (2007).
[CrossRef]

T. M. Jorgensen, 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, 041208 (2007).
[CrossRef]

A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. R. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction,” Opt. Express 15, 6200–6209 (2007).
[CrossRef]

P. Puvanathasan and K. Bizheva, “Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set,” Opt. Express 15, 15747–15758 (2007).
[CrossRef]

2005

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transverse blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
[CrossRef]

2004

2003

M. Pircher, E. Gotzinger, R. Leitgeb, A. F. Fercher, and C. K. Hitzenberger, “Speckle reduction in optical coherence tomography by frequency compounding,” J. Biomed. Opt. 8, 565–569 (2003).
[CrossRef]

1999

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

1991

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

1976

Adler, D. C.

April, G.

Araujo, A.

Arsenault, H. H.

Aubert, G.

G. Aubert and J. Aujol, “A variational approach to remove multiplicative noise,” SIAM J. Appl. Math. 68, 925–946 (2008).
[CrossRef]

Aujol, J.

G. Aubert and J. Aujol, “A variational approach to remove multiplicative noise,” SIAM J. Appl. Math. 68, 925–946 (2008).
[CrossRef]

Barbeiro, S.

Bernardes, R.

Bezerrab, H. G.

Z. Wang, H. Kyonob, H. G. Bezerrab, D. L. Wilson, M. A. Costa, and A. M. Rollins, “Automatic segmentation of intravascular optical coherence tomography images for facilitating quantitative diagnosis of atherosclerosis,” Proc. SPIE 7889, 78890N(2011).
[CrossRef]

Bhattacharya, S.

R. Prashanth and S. Bhattacharya, “Space variant deconvolution for optical coherence tomography,” Proc. SPIE 8311, 831113 (2011).
[CrossRef]

Bioucas-Dias, J. M.

J. M. Bioucas-Dias and M. A. T. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Trans. Image Process. 19, 1720–1730 (2010).
[CrossRef]

M. A. T. Figueiredo and J. M. Bioucas-Dias, “Restoration of Poissonian images using alternating direction optimization,” IEEE Trans. Image Process. 19, 3133–3145 (2010).
[CrossRef]

Bizheva, K.

Boppart, S. A.

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transverse blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
[CrossRef]

Borsdorf, A.

Bouma, B. E.

Caneiro, D. A.

D. A. Caneiro, S. A. Read, and M. J. Collins, “Speckle reduction in optical coherence tomography imaging by affine-motion image registration,” J. Biomed. Opt. 16, 116027 (2011).
[CrossRef]

Chan, T. F.

E. Esser, X. Q. Zhang, and T. F. Chan, “A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science,” SIAM J. Imaging Sci. 3, 1015–1046 (2010).
[CrossRef]

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

Chen, D. Q.

D. Q. Chen and L. Z. Cheng, “Spatially adapted regularization parameter selection based on the local discrepancy function for Poissonian image deblurring,” Inverse Probl. 28, 015004 (2012).
[CrossRef]

Chen, Z.

Cheng, L. Z.

D. Q. Chen and L. Z. Cheng, “Spatially adapted regularization parameter selection based on the local discrepancy function for Poissonian image deblurring,” Inverse Probl. 28, 015004 (2012).
[CrossRef]

Chitchian, S.

S. Chitchian, M. A. Fiddy, and N. M. Fried, “Denoising during optical coherence tomography of the prostate nerves via wavelet shrinkage using dual-tree complex wavelet transform,” J. Biomed. Opt. 14, 014031 (2009).
[CrossRef]

Christensen, U.

T. M. Jorgensen, 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, 041208 (2007).
[CrossRef]

Collins, M. J.

D. A. Caneiro, S. A. Read, and M. J. Collins, “Speckle reduction in optical coherence tomography imaging by affine-motion image registration,” J. Biomed. Opt. 16, 116027 (2011).
[CrossRef]

Costa, M. A.

Z. Wang, H. Kyonob, H. G. Bezerrab, D. L. Wilson, M. A. Costa, and A. M. Rollins, “Automatic segmentation of intravascular optical coherence tomography images for facilitating quantitative diagnosis of atherosclerosis,” Proc. SPIE 7889, 78890N(2011).
[CrossRef]

Cunha-Vaz, J.

Dabov, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

Danielyan, A.

A. Danielyan, V. Katkovnik, and K. Egiazarian, “BM3D frames and variational image deblurring,” IEEE Trans. Image Process. 21, 1715–1728 (2012).
[CrossRef]

A. Danielyan, V. Katkovnik, and K. Egiazarian, “Deblurring of Poissonian images using BM3D frames,” Proc. SPIE 8138, 813812 (2011).
[CrossRef]

Darbon, J.

L. Denis, F. Tupin, J. Darbon, and M. Sigelle, “SAR image regularization with fast approximate discrete minimization,” IEEE Trans. Image Process. 18, 1588–1600 (2009).
[CrossRef]

Denis, L.

L. Denis, F. Tupin, J. Darbon, and M. Sigelle, “SAR image regularization with fast approximate discrete minimization,” IEEE Trans. Image Process. 18, 1588–1600 (2009).
[CrossRef]

Desjardins, A. E.

Dong, F.

F. Dong, H. Zhang, and D. X. Kong, “Nonlocal total variation models for multiplicative noise removal using split Bregman iteration,” Math. Comput. Model. 55, 939–954(2012).
[CrossRef]

Dong, W.

W. Dong, X. Li, L. Zhang, and G. Shi, “Sparsity-based image denoising via dictionary learning and structural clustering,” in Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 457–464.

Egiazarian, K.

A. Danielyan, V. Katkovnik, and K. Egiazarian, “BM3D frames and variational image deblurring,” IEEE Trans. Image Process. 21, 1715–1728 (2012).
[CrossRef]

A. Danielyan, V. Katkovnik, and K. Egiazarian, “Deblurring of Poissonian images using BM3D frames,” Proc. SPIE 8138, 813812 (2011).
[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

Esser, E.

E. Esser, X. Q. Zhang, and T. F. Chan, “A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science,” SIAM J. Imaging Sci. 3, 1015–1046 (2010).
[CrossRef]

Fang, L.

Farsiu, S.

Fercher, A. F.

M. Pircher, E. Gotzinger, R. Leitgeb, A. F. Fercher, and C. K. Hitzenberger, “Speckle reduction in optical coherence tomography by frequency compounding,” J. Biomed. Opt. 8, 565–569 (2003).
[CrossRef]

Fernandez, D. C.

H. M. Salinas and D. C. Fernandez, “Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography,” IEEE Trans. Med. Imaging 26, 761–771 (2007).
[CrossRef]

Fiddy, M. A.

S. Chitchian, M. A. Fiddy, and N. M. Fried, “Denoising during optical coherence tomography of the prostate nerves via wavelet shrinkage using dual-tree complex wavelet transform,” J. Biomed. Opt. 14, 014031 (2009).
[CrossRef]

Figueiredo, M. A. T.

J. M. Bioucas-Dias and M. A. T. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Trans. Image Process. 19, 1720–1730 (2010).
[CrossRef]

M. A. T. Figueiredo and J. M. Bioucas-Dias, “Restoration of Poissonian images using alternating direction optimization,” IEEE Trans. Image Process. 19, 3133–3145 (2010).
[CrossRef]

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

Fried, N. M.

S. Chitchian, M. A. Fiddy, and N. M. Fried, “Denoising during optical coherence tomography of the prostate nerves via wavelet shrinkage using dual-tree complex wavelet transform,” J. Biomed. Opt. 14, 014031 (2009).
[CrossRef]

Fujimoto, J. G.

D. C. Adler, T. H. Ko, and J. G. Fujimoto, “Speckle reduction in optical coherence tomography images by use of a spatially adaptive wavelet filter,” Opt. Lett. 29, 2878–2880 (2004).
[CrossRef]

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

Gilboa, G.

G. Gilboa and S. Osher, “Nonlocal operators with applications to image processing,” Multiscale Model. Simul. 7, 1005–1028 (2009).
[CrossRef]

Gorczynska, I.

Gotzinger, E.

M. Pircher, E. Gotzinger, R. Leitgeb, A. F. Fercher, and C. K. Hitzenberger, “Speckle reduction in optical coherence tomography by frequency compounding,” J. Biomed. Opt. 8, 565–569 (2003).
[CrossRef]

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

Gu, Y.

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

Guo, J.

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

Hitzenberger, C. K.

M. Pircher, E. Gotzinger, R. Leitgeb, A. F. Fercher, and C. K. Hitzenberger, “Speckle reduction in optical coherence tomography by frequency compounding,” J. Biomed. Opt. 8, 565–569 (2003).
[CrossRef]

Hornegger, J.

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

Huang, N. Y.

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

Izatt, J. A.

Jian, Z.

Jorgensen, T. M.

T. M. Jorgensen, 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, 041208 (2007).
[CrossRef]

Kamalabadi, F.

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transverse blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
[CrossRef]

Kang, J. U.

Katkovnik, V.

A. Danielyan, V. Katkovnik, and K. Egiazarian, “BM3D frames and variational image deblurring,” IEEE Trans. Image Process. 21, 1715–1728 (2012).
[CrossRef]

A. Danielyan, V. Katkovnik, and K. Egiazarian, “Deblurring of Poissonian images using BM3D frames,” Proc. SPIE 8138, 813812 (2011).
[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

Ko, T. H.

Kong, D. X.

F. Dong, H. Zhang, and D. X. Kong, “Nonlocal total variation models for multiplicative noise removal using split Bregman iteration,” Math. Comput. Model. 55, 939–954(2012).
[CrossRef]

Kowalczyk, A.

Kyonob, H.

Z. Wang, H. Kyonob, H. G. Bezerrab, D. L. Wilson, M. A. Costa, and A. M. Rollins, “Automatic segmentation of intravascular optical coherence tomography images for facilitating quantitative diagnosis of atherosclerosis,” Proc. SPIE 7889, 78890N(2011).
[CrossRef]

Leitgeb, R.

M. Pircher, E. Gotzinger, R. Leitgeb, A. F. Fercher, and C. K. Hitzenberger, “Speckle reduction in optical coherence tomography by frequency compounding,” J. Biomed. Opt. 8, 565–569 (2003).
[CrossRef]

Li, S.

Li, X.

W. Dong, X. Li, L. Zhang, and G. Shi, “Sparsity-based image denoising via dictionary learning and structural clustering,” in Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 457–464.

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

Liu, Y.

Maduro, C.

Mardin, C. Y.

Marks, D. L.

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transverse blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
[CrossRef]

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Motaghiannezam, S. M. R.

Mu, G.

Natterer, F.

F. Natterer and F. Wubbeling, Mathematical Methods in Image Reconstruction (SIAM, 2001).

Nie, Q.

Oh, W. Y.

Osher, S.

G. Gilboa and S. Osher, “Nonlocal operators with applications to image processing,” Multiscale Model. Simul. 7, 1005–1028 (2009).
[CrossRef]

J. Shi and S. Osher, “A nonlinear inverse scale space method for a convex multiplicative noise model,” SIAM J. Imaging Sci. 1, 294–321 (2008).
[CrossRef]

Pircher, M.

M. Pircher, E. Gotzinger, R. Leitgeb, A. F. Fercher, and C. K. Hitzenberger, “Speckle reduction in optical coherence tomography by frequency compounding,” J. Biomed. Opt. 8, 565–569 (2003).
[CrossRef]

Prashanth, R.

R. Prashanth and S. Bhattacharya, “Space variant deconvolution for optical coherence tomography,” Proc. SPIE 8311, 831113 (2011).
[CrossRef]

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

Puvanathasan, P.

Qiu, H. X.

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

Ralston, T. S.

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transverse blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
[CrossRef]

Rao, B.

Read, S. A.

D. A. Caneiro, S. A. Read, and M. J. Collins, “Speckle reduction in optical coherence tomography imaging by affine-motion image registration,” J. Biomed. Opt. 16, 116027 (2011).
[CrossRef]

Rollins, A. M.

Z. Wang, H. Kyonob, H. G. Bezerrab, D. L. Wilson, M. A. Costa, and A. M. Rollins, “Automatic segmentation of intravascular optical coherence tomography images for facilitating quantitative diagnosis of atherosclerosis,” Proc. SPIE 7889, 78890N(2011).
[CrossRef]

Salinas, H. M.

H. M. Salinas and D. C. Fernandez, “Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography,” IEEE Trans. Med. Imaging 26, 761–771 (2007).
[CrossRef]

Sander, B.

T. M. Jorgensen, 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, 041208 (2007).
[CrossRef]

Schmitt, J. M.

J. M. Schmitt, S. H. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. 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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

Serranho, P.

Setzer, S.

S. Setzer, “Operator splittings, Bregman methods and frame shrinkage in image processing,” Int. J. Comput. Vis. 92, 265–280 (2011).
[CrossRef]

Shen, T. M.

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

Shi, G.

W. Dong, X. Li, L. Zhang, and G. Shi, “Sparsity-based image denoising via dictionary learning and structural clustering,” in Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 457–464.

Shi, J.

J. Shi and S. Osher, “A nonlinear inverse scale space method for a convex multiplicative noise model,” SIAM J. Imaging Sci. 1, 294–321 (2008).
[CrossRef]

Sigelle, M.

L. Denis, F. Tupin, J. Darbon, and M. Sigelle, “SAR image regularization with fast approximate discrete minimization,” IEEE Trans. Image Process. 18, 1588–1600 (2009).
[CrossRef]

Soliman, W.

T. M. Jorgensen, 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, 041208 (2007).
[CrossRef]

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

Sylwestrzak, M.

Szkulmowski, M.

Szlag, D.

Tearney, G. J.

Thomadsen, J.

T. M. Jorgensen, 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, 041208 (2007).
[CrossRef]

Tornow, R. P.

Toth, C. A.

Tromberg, B. J.

Tupin, F.

L. Denis, F. Tupin, J. Darbon, and M. Sigelle, “SAR image regularization with fast approximate discrete minimization,” IEEE Trans. Image Process. 18, 1588–1600 (2009).
[CrossRef]

Vakoc, B. J.

Wagner, M.

Wang, T. S.

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

Wang, Z.

Z. Wang, H. Kyonob, H. G. Bezerrab, D. L. Wilson, M. A. Costa, and A. M. Rollins, “Automatic segmentation of intravascular optical coherence tomography images for facilitating quantitative diagnosis of atherosclerosis,” Proc. SPIE 7889, 78890N(2011).
[CrossRef]

Wei, X. B.

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

Wilson, D. L.

Z. Wang, H. Kyonob, H. G. Bezerrab, D. L. Wilson, M. A. Costa, and A. M. Rollins, “Automatic segmentation of intravascular optical coherence tomography images for facilitating quantitative diagnosis of atherosclerosis,” Proc. SPIE 7889, 78890N(2011).
[CrossRef]

Wojtkowski, M.

Wubbeling, F.

F. Natterer and F. Wubbeling, Mathematical Methods in Image Reconstruction (SIAM, 2001).

Xiang, S. H.

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

Xue, P.

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

Yu, L.

Yu, X.

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

Yung, K. M.

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

Zhang, H.

F. Dong, H. Zhang, and D. X. Kong, “Nonlocal total variation models for multiplicative noise removal using split Bregman iteration,” Math. Comput. Model. 55, 939–954(2012).
[CrossRef]

Zhang, K.

Zhang, L.

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

W. Dong, X. Li, L. Zhang, and G. Shi, “Sparsity-based image denoising via dictionary learning and structural clustering,” in Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 457–464.

Zhang, X. Q.

E. Esser, X. Q. Zhang, and T. F. Chan, “A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science,” SIAM J. Imaging Sci. 3, 1015–1046 (2010).
[CrossRef]

Zhao, S. Y.

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

Zhu, X.

Biomed. Opt. Express

IEEE Trans. Image Process.

T. S. Ralston, D. L. Marks, F. Kamalabadi, and S. A. Boppart, “Deconvolution methods for mitigation of transverse blurring in optical coherence tomography,” IEEE Trans. Image Process. 14, 1254–1264 (2005).
[CrossRef]

J. M. Bioucas-Dias and M. A. T. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Trans. Image Process. 19, 1720–1730 (2010).
[CrossRef]

M. A. T. Figueiredo and J. M. Bioucas-Dias, “Restoration of Poissonian images using alternating direction optimization,” IEEE Trans. Image Process. 19, 3133–3145 (2010).
[CrossRef]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

A. Danielyan, V. Katkovnik, and K. Egiazarian, “BM3D frames and variational image deblurring,” IEEE Trans. Image Process. 21, 1715–1728 (2012).
[CrossRef]

L. Denis, F. Tupin, J. Darbon, and M. Sigelle, “SAR image regularization with fast approximate discrete minimization,” IEEE Trans. Image Process. 18, 1588–1600 (2009).
[CrossRef]

IEEE Trans. Med. Imaging

H. M. Salinas and D. C. Fernandez, “Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography,” IEEE Trans. Med. Imaging 26, 761–771 (2007).
[CrossRef]

Int. J. Comput. Vis.

S. Setzer, “Operator splittings, Bregman methods and frame shrinkage in image processing,” Int. J. Comput. Vis. 92, 265–280 (2011).
[CrossRef]

Inverse Probl.

D. Q. Chen and L. Z. Cheng, “Spatially adapted regularization parameter selection based on the local discrepancy function for Poissonian image deblurring,” Inverse Probl. 28, 015004 (2012).
[CrossRef]

J. Biomed. Opt.

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

M. Pircher, E. Gotzinger, R. Leitgeb, A. F. Fercher, and C. K. Hitzenberger, “Speckle reduction in optical coherence tomography by frequency compounding,” J. Biomed. Opt. 8, 565–569 (2003).
[CrossRef]

T. M. Jorgensen, 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, 041208 (2007).
[CrossRef]

D. A. Caneiro, S. A. Read, and M. J. Collins, “Speckle reduction in optical coherence tomography imaging by affine-motion image registration,” J. Biomed. Opt. 16, 116027 (2011).
[CrossRef]

S. Chitchian, M. A. Fiddy, and N. M. Fried, “Denoising during optical coherence tomography of the prostate nerves via wavelet shrinkage using dual-tree complex wavelet transform,” J. Biomed. Opt. 14, 014031 (2009).
[CrossRef]

S. Y. Zhao, Y. Gu, P. Xue, J. Guo, T. M. Shen, T. S. Wang, N. Y. Huang, L. Zhang, H. X. Qiu, X. Yu, and X. B. Wei, “Imaging port wine stains by fiber optical coherence tomography,” J. Biomed. Opt. 15, 036020 (2010).
[CrossRef]

J. Opt. Soc. Am.

J. Opt. Soc. Am. A

Math. Comput. Model.

F. Dong, H. Zhang, and D. X. Kong, “Nonlocal total variation models for multiplicative noise removal using split Bregman iteration,” Math. Comput. Model. 55, 939–954(2012).
[CrossRef]

Multiscale Model. Simul.

G. Gilboa and S. Osher, “Nonlocal operators with applications to image processing,” Multiscale Model. Simul. 7, 1005–1028 (2009).
[CrossRef]

Opt. Express

Opt. Lett.

Proc. SPIE

Z. Wang, H. Kyonob, H. G. Bezerrab, D. L. Wilson, M. A. Costa, and A. M. Rollins, “Automatic segmentation of intravascular optical coherence tomography images for facilitating quantitative diagnosis of atherosclerosis,” Proc. SPIE 7889, 78890N(2011).
[CrossRef]

R. Prashanth and S. Bhattacharya, “Space variant deconvolution for optical coherence tomography,” Proc. SPIE 8311, 831113 (2011).
[CrossRef]

A. Danielyan, V. Katkovnik, and K. Egiazarian, “Deblurring of Poissonian images using BM3D frames,” Proc. SPIE 8138, 813812 (2011).
[CrossRef]

Science

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 J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991).
[CrossRef]

SIAM J. Appl. Math.

G. Aubert and J. Aujol, “A variational approach to remove multiplicative noise,” SIAM J. Appl. Math. 68, 925–946 (2008).
[CrossRef]

SIAM J. Imaging Sci.

J. Shi and S. Osher, “A nonlinear inverse scale space method for a convex multiplicative noise model,” SIAM J. Imaging Sci. 1, 294–321 (2008).
[CrossRef]

E. Esser, X. Q. Zhang, and T. F. Chan, “A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science,” SIAM J. Imaging Sci. 3, 1015–1046 (2010).
[CrossRef]

Other

W. Dong, X. Li, L. Zhang, and G. Shi, “Sparsity-based image denoising via dictionary learning and structural clustering,” in Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 457–464.

F. Natterer and F. Wubbeling, Mathematical Methods in Image Reconstruction (SIAM, 2001).

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Figures (4)

Fig. 1.
Fig. 1.

Result example 1 from the human skin dataset generated from six consecutive frames: (a) one frame with regions of interest overlaid in green, (b) registered and averaged, (c) deconvolved using the proposed algorithm on raw OCT data, and (d) deconvolved using the BM3D-deconvolution algorithm in [29] on log-compressed OCT data. (e)–(h) Four zoomed images of the skin in the red rectangular boxes of (a)–(d) for a closer view of the performance of the algorithms.

Fig. 2.
Fig. 2.

Result example 2 from the human skin dataset generated from six consecutive frames: (a) one frame with regions of interest overlaid in green, (b) registered and averaged, (c) deconvolved using the proposed algorithm on raw OCT data, and (d) deconvolved using the BM3D-deconvolution algorithm in [29] on log-compressed OCT data. (e)–(h) Four zoomed images of the skin in the red rectangular boxes of (a)–(d) for a closer view of the performance of the algorithms.

Fig. 3.
Fig. 3.

Example 1 in Fig. 1: (a) CNR and ENL for OCT images restored by Algorithm 1 with different τ and (b) SNR and K for OCT images restored by Algorithm 1 with different τ.

Fig. 4.
Fig. 4.

Example 2 in Fig. 2: (a) CNR and ENL for OCT images restored by Algorithm 1 with different τ and (b) SNR and K for OCT images restored by Algorithm 1 with different τ.

Tables (2)

Tables Icon

Table 1. Image Quality Metrics for Human Skin OCT Images in Fig. 1

Tables Icon

Table 2. Image Quality Metrics for Human Skin OCT Images in Fig. 2

Equations (13)

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

Pu(f)=π2f(Hu)2exp[π4f2(Hu)2],
Pu(f)=2κ2MΓ(M)f(2M1)(Hu)2Mexp[κ2f2(Hu)2],
minu>0{τϕ(u)+Ω[12(fHu)2+log(Hu)]dx}.
minu>0{τϕ(u)+i,j=1n[12(fHu)2+log(Hu)]i,j}.
minu,v,w,z{τϕ(v)+i,j=1n[12(fw)2+log(w)]i,j+ιR+(z)|u=v=z,Hu=w},
Lu,ρδ=F(s)+ρ,Gus+δ2Gus22,
uk+1=(HTH+2I)1(vk+d1k+HT(wk+d2k)+zk+d3k).
vk+1=argminv{τΦvp+δ2v(uk+1d1k)2},
vk+1=ΨSτδ(Φ(uk+1d1k)),
CNR=1Mm=1M(μmμb)σm2+σb2,
ENL=1Hm=1Hμh2σh2,
SNR=10lg[max(I2)σ2],
K=IpeakIside,

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