Abstract

Optical coherence tomography (OCT) is an important imaging technique extensively applied in medical sciences. However, OCT images often suffer from speckle noise, which is a kind of multiplicative noise inherited in coherent imaging systems. A speckle noise reduction algorithm with total variation (TV) regularization is proposed to restore speckled OCT images. It constructs the regularization parameter and utilizes the tuning function. The proposed algorithm realizes sufficient speckle noise reduction and delicate edge preservation. Simulations demonstrate the performance of the proposed algorithm with respect to visual effects, processing time and image quality metrics of signal-to-noise ratio (SNR), equivalent number of looks (ENL), contrast-to-noise ratio (CNR), relative mean square error (RMSE) and edge preservation factor. Compared with some classical and typical despeckling algorithms, the proposed algorithm exhibits good results in edge preservation, recovery error and time efficiency, and presents better SNR, ENL and CNR. The applicability of the proposed algorithm with regard to OCT in-device preprocessing is discussed in details. Therefore, it promotes the application of OCT imaging in clinical diagnosis and analysis.

© 2015 Optical Society of America

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References

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2014 (1)

2013 (2)

2012 (1)

2010 (3)

2008 (2)

M. Gargesha, M. W. Jenkins, A. M. Rollins, and D. L. Wilson, “Denoising and 4D visualization of OCT images,” Opt. Express 16(16), 12313–12333 (2008).
[Crossref] [PubMed]

G. Aubert and J. F. Aujol, “A variational approach to removing multiplicative noise,” SIAM J. Appl. Math. 68(4), 925–946 (2008).
[Crossref]

2007 (3)

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A 22(7), 1901–1910 (2007).
[Crossref]

A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction,” Opt. Express 15(10), 6200–6209 (2007).
[Crossref] [PubMed]

D. P. Popescu, M. D. Hewko, and M. G. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Commun. 269(1), 247–251 (2007).
[Crossref]

2006 (3)

A. Ozcan, M. J. F. Digonnet, and G. S. Kino, “Minimum-phase-function-based processing in frequency-domain optical coherence tomography systems,” J. Opt. Soc. Am. A 23(7), 1669–1677 (2006).
[Crossref]

S. Aja-Fernández and C. Alberola-López, “On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering,” IEEE Trans. Image Process. 15(9), 2694–2701 (2006).
[Crossref] [PubMed]

E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inform. Theory 52(2), 489–509 (2006).
[Crossref]

2005 (1)

2004 (1)

2003 (2)

N. Iftimia, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography by ‘path length encoded’ angular compounding,” J. Biomed. Opt. 8(2), 260–263 (2003).
[Crossref] [PubMed]

A. Pižurica, W. Philips, I. Lemahieu, and M. Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging,” IEEE Trans. Med. Imag. 22(3), 323–331 (2003).
[Crossref]

2002 (1)

Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. 11(11), 1260–1270 (2002).
[Crossref]

2000 (1)

1997 (2)

J. M. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol. 42(7), 1427–1439 (1997).
[Crossref] [PubMed]

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process. 6(6), 888–895 (1997).
[Crossref] [PubMed]

1995 (3)

D. L. Donoho and M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” J. Amer. Statist. Assoc. 90(432), 1200–1224 (1995).
[Crossref]

A. F. Fercher, C. K. Hitzenberger, G. Kamp, and S. Y. El-Zaiat, “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117(1), 43–48 (1995).
[Crossref]

G. Franceschetti, V. Pascazio, and G. Schirinzi, “Iterative homomorphic technique for speckle reduction in synthetic-aperture radar imaging,” J. Opt. Soc. Am. A 12(4), 686–694 (1995).
[Crossref]

1992 (1)

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60(1), 259–268 (1992).
[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 J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

1987 (1)

D. T. Kuan, A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust., Speech, Signal Process. 35(3), 373–383 (1987).
[Crossref]

1986 (2)

J. S. Lee, “Speckle suppression and analysis for synthetic aperture radar images,” Opt. Eng. 25(5), 636–643 (1986).
[Crossref]

F. T. Ulaby, F. Kouyate, B. Brisco, and T. H. L. Williams, “Textural information in SAR images,” IEEE Trans. Geosci. Remote Sensing 24(2), 235–245 (1986).
[Crossref]

1984 (1)

D. Geman and S. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984).
[Crossref] [PubMed]

1982 (1)

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 157–166 (1982).
[Crossref] [PubMed]

Acheroy, M.

A. Pižurica, W. Philips, I. Lemahieu, and M. Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging,” IEEE Trans. Med. Imag. 22(3), 323–331 (2003).
[Crossref]

Acton, S. T.

Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. 11(11), 1260–1270 (2002).
[Crossref]

Adler, D. C.

Aja-Fernández, S.

S. Aja-Fernández and C. Alberola-López, “On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering,” IEEE Trans. Image Process. 15(9), 2694–2701 (2006).
[Crossref] [PubMed]

Alberola-López, C.

S. Aja-Fernández and C. Alberola-López, “On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering,” IEEE Trans. Image Process. 15(9), 2694–2701 (2006).
[Crossref] [PubMed]

Andersen, P. E.

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in European Conference on Biomedical Optics, (Optical Society of America, 2007), pp. 66270P.

Araújo, A.

Aubert, G.

G. Aubert and J. F. Aujol, “A variational approach to removing multiplicative noise,” SIAM J. Appl. Math. 68(4), 925–946 (2008).
[Crossref]

G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Springer Science & Business Media, 2006).

Aujol, J. F.

G. Aubert and J. F. Aujol, “A variational approach to removing multiplicative noise,” SIAM J. Appl. Math. 68(4), 925–946 (2008).
[Crossref]

Avanaki, M. R. N.

Barbeiro, S.

Bashkansky, M.

Baumann, B.

B. Heise, E. Leiss-Holzinger, M. Pircher, E. Götzinger, B. Baumann, C. K. Hitzenberger, and D. Stifter, “Advanced image processing of retardation scans for polarization-sensitive optical coherence tomography,” in European Conference on Biomedical Optics, (Optical Society of America, 2009), pp. 73720S.

Bernardes, R.

Bilenca, A.

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A 22(7), 1901–1910 (2007).
[Crossref]

Borsdorf, A.

Bouma, B. E.

A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction,” Opt. Express 15(10), 6200–6209 (2007).
[Crossref] [PubMed]

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A 22(7), 1901–1910 (2007).
[Crossref]

N. Iftimia, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography by ‘path length encoded’ angular compounding,” J. Biomed. Opt. 8(2), 260–263 (2003).
[Crossref] [PubMed]

Brisco, B.

F. T. Ulaby, F. Kouyate, B. Brisco, and T. H. L. Williams, “Textural information in SAR images,” IEEE Trans. Geosci. Remote Sensing 24(2), 235–245 (1986).
[Crossref]

Cai, H.

H. Tian, H. Cai, J. Lai, and X. Xu, “Effective image noise removal based on difference eigenvalue,” in Proceedings of ICIP 2011 International Conference on Image Processing (Brussels, 2011).
[Crossref]

Candès, E.

E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inform. Theory 52(2), 489–509 (2006).
[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(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Chavel, P.

D. T. Kuan, A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust., Speech, Signal Process. 35(3), 373–383 (1987).
[Crossref]

Conroy, L.

Cunha-Vaz, J.

DaCosta, R. S.

Desjardins, A. E.

A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction,” Opt. Express 15(10), 6200–6209 (2007).
[Crossref] [PubMed]

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A 22(7), 1901–1910 (2007).
[Crossref]

Digonnet, M. J. F.

Donoho, D. L.

D. L. Donoho and M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” J. Amer. Statist. Assoc. 90(432), 1200–1224 (1995).
[Crossref]

El-Zaiat, S. Y.

A. F. Fercher, C. K. Hitzenberger, G. Kamp, and S. Y. El-Zaiat, “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117(1), 43–48 (1995).
[Crossref]

Eom, T. J.

Farhat, G.

Fatemi, E.

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

Fercher, A. F.

A. F. Fercher, C. K. Hitzenberger, G. Kamp, and S. Y. El-Zaiat, “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117(1), 43–48 (1995).
[Crossref]

Fernández, D. C.

Floreby, L.

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process. 6(6), 888–895 (1997).
[Crossref] [PubMed]

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(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Flueraru, C.

Franceschetti, G.

Frost, V. S.

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 157–166 (1982).
[Crossref] [PubMed]

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(24), 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(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Gargesha, M.

Geman, D.

D. Geman and S. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984).
[Crossref] [PubMed]

Geman, S.

D. Geman and S. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984).
[Crossref] [PubMed]

Gong, G.

Goodman, J. R.

J. R. Goodman and R. L. Haupt, Statistical Optics (John Wiley & Sons, 2015).

Goodman, J. W.

J. W. Goodman, “Statistical properties of laser speckle patterns,” in Laser Speckle and Related Phenomena, J. C. Dainty, ed. (Springer, 1975), pp. 9–75.

Götzinger, E.

B. Heise, E. Leiss-Holzinger, M. Pircher, E. Götzinger, B. Baumann, C. K. Hitzenberger, and D. Stifter, “Advanced image processing of retardation scans for polarization-sensitive optical coherence tomography,” in European Conference on Biomedical Optics, (Optical Society of America, 2009), pp. 73720S.

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(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Haupt, R. L.

J. R. Goodman and R. L. Haupt, Statistical Optics (John Wiley & Sons, 2015).

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(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Heise, B.

B. Heise, E. Leiss-Holzinger, M. Pircher, E. Götzinger, B. Baumann, C. K. Hitzenberger, and D. Stifter, “Advanced image processing of retardation scans for polarization-sensitive optical coherence tomography,” in European Conference on Biomedical Optics, (Optical Society of America, 2009), pp. 73720S.

Hewko, M. D.

D. P. Popescu, M. D. Hewko, and M. G. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Commun. 269(1), 247–251 (2007).
[Crossref]

Hitzenberger, C. K.

A. F. Fercher, C. K. Hitzenberger, G. Kamp, and S. Y. El-Zaiat, “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117(1), 43–48 (1995).
[Crossref]

B. Heise, E. Leiss-Holzinger, M. Pircher, E. Götzinger, B. Baumann, C. K. Hitzenberger, and D. Stifter, “Advanced image processing of retardation scans for polarization-sensitive optical coherence tomography,” in European Conference on Biomedical Optics, (Optical Society of America, 2009), pp. 73720S.

Hojjatoleslami, A.

Holtzman, J.

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 157–166 (1982).
[Crossref] [PubMed]

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(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Iftimia, N.

N. Iftimia, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography by ‘path length encoded’ angular compounding,” J. Biomed. Opt. 8(2), 260–263 (2003).
[Crossref] [PubMed]

Iwanicka, M.

P. Targowski, M. Iwanicka, L. Tymińska-Widmer, M. Sylwestrzak, and E. A. Kwiatkowska, “Structural examination of easel paintings with optical coherence tomography,” Acc. Chem. Res. 43(6), 826–836 (2010).
[Crossref] [PubMed]

Jenkins, M. W.

Johnstone, M.

D. L. Donoho and M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” J. Amer. Statist. Assoc. 90(432), 1200–1224 (1995).
[Crossref]

Jørgensen, T. M.

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in European Conference on Biomedical Optics, (Optical Society of America, 2007), pp. 66270P.

Kamp, G.

A. F. Fercher, C. K. Hitzenberger, G. Kamp, and S. Y. El-Zaiat, “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117(1), 43–48 (1995).
[Crossref]

Kino, G. S.

Ko, T. H.

Kornprobst, P.

G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Springer Science & Business Media, 2006).

Kouyate, F.

F. T. Ulaby, F. Kouyate, B. Brisco, and T. H. L. Williams, “Textural information in SAR images,” IEEE Trans. Geosci. Remote Sensing 24(2), 235–245 (1986).
[Crossref]

Kuan, D. T.

D. T. Kuan, A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust., Speech, Signal Process. 35(3), 373–383 (1987).
[Crossref]

Kwiatkowska, E. A.

P. Targowski, M. Iwanicka, L. Tymińska-Widmer, M. Sylwestrzak, and E. A. Kwiatkowska, “Structural examination of easel paintings with optical coherence tomography,” Acc. Chem. Res. 43(6), 826–836 (2010).
[Crossref] [PubMed]

Lai, J.

H. Tian, H. Cai, J. Lai, and X. Xu, “Effective image noise removal based on difference eigenvalue,” in Proceedings of ICIP 2011 International Conference on Image Processing (Brussels, 2011).
[Crossref]

Laissue, P. P.

Lee, J. S.

J. S. Lee, “Speckle suppression and analysis for synthetic aperture radar images,” Opt. Eng. 25(5), 636–643 (1986).
[Crossref]

Leiss-Holzinger, E.

B. Heise, E. Leiss-Holzinger, M. Pircher, E. Götzinger, B. Baumann, C. K. Hitzenberger, and D. Stifter, “Advanced image processing of retardation scans for polarization-sensitive optical coherence tomography,” in European Conference on Biomedical Optics, (Optical Society of America, 2009), pp. 73720S.

Lemahieu, I.

A. Pižurica, W. Philips, I. Lemahieu, and M. Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging,” IEEE Trans. Med. Imag. 22(3), 323–331 (2003).
[Crossref]

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(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Lindenmaier, A. A.

Lions, P. L.

L. Rudin, P. L. Lions, and S. Osher, “Multiplicative denoising and deblurring: theory and algorithms,” in Geometric Level Set Methods in Imaging, Vision, and Graphics, S. Osher and N. Paragios, eds. (Springer, 2003), pp. 103–119.
[Crossref]

Lovstrom, B.

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process. 6(6), 888–895 (1997).
[Crossref] [PubMed]

Maduro, C.

Mardin, C. Y.

Mayer, M. A.

Meyer, Y.

Y. Meyer, “Oscillating patterns in image processing and nonlinear evolution equations,” in the fifteenth Dean Jacqueline B. Lewis memorial lectures (Vol. 22) (American Mathematical Society, 2001).

Mogensen, M.

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in European Conference on Biomedical Optics, (Optical Society of America, 2007), pp. 66270P.

Motaghiannezam, S. M.

Oh, W. Y.

Osher, S.

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

L. Rudin, P. L. Lions, and S. Osher, “Multiplicative denoising and deblurring: theory and algorithms,” in Geometric Level Set Methods in Imaging, Vision, and Graphics, S. Osher and N. Paragios, eds. (Springer, 2003), pp. 103–119.
[Crossref]

Ozcan, A.

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A 22(7), 1901–1910 (2007).
[Crossref]

A. Ozcan, M. J. F. Digonnet, and G. S. Kino, “Minimum-phase-function-based processing in frequency-domain optical coherence tomography systems,” J. Opt. Soc. Am. A 23(7), 1669–1677 (2006).
[Crossref]

Pascazio, V.

Pedersen, F.

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in European Conference on Biomedical Optics, (Optical Society of America, 2007), pp. 66270P.

Philips, W.

A. Pižurica, W. Philips, I. Lemahieu, and M. Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging,” IEEE Trans. Med. Imag. 22(3), 323–331 (2003).
[Crossref]

Pircher, M.

B. Heise, E. Leiss-Holzinger, M. Pircher, E. Götzinger, B. Baumann, C. K. Hitzenberger, and D. Stifter, “Advanced image processing of retardation scans for polarization-sensitive optical coherence tomography,” in European Conference on Biomedical Optics, (Optical Society of America, 2009), pp. 73720S.

Pižurica, A.

A. Pižurica, W. Philips, I. Lemahieu, and M. Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging,” IEEE Trans. Med. Imag. 22(3), 323–331 (2003).
[Crossref]

Podoleanu, A. G.

Popescu, D. P.

D. P. Popescu, M. D. Hewko, and M. G. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Commun. 269(1), 247–251 (2007).
[Crossref]

Puliafito, C. A.

D. C. Fernández, H. M. Salinas, and C. A. Puliafito, “Automated detection of retinal layer structures on optical coherence tomography images,” Opt. Express 13(25), 10200–10216 (2005).
[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(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Reintjes, J.

Rollins, A. M.

Romberg, J.

E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inform. Theory 52(2), 489–509 (2006).
[Crossref]

Rudin, L.

L. Rudin, P. L. Lions, and S. Osher, “Multiplicative denoising and deblurring: theory and algorithms,” in Geometric Level Set Methods in Imaging, Vision, and Graphics, S. Osher and N. Paragios, eds. (Springer, 2003), pp. 103–119.
[Crossref]

Rudin, L. I.

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

Salinas, H. M.

Salomonsson, G.

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process. 6(6), 888–895 (1997).
[Crossref] [PubMed]

Sattar, F.

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process. 6(6), 888–895 (1997).
[Crossref] [PubMed]

Sawchuk, A.

D. T. Kuan, A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust., Speech, Signal Process. 35(3), 373–383 (1987).
[Crossref]

Schirinzi, G.

Schmitt, J. M.

J. M. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol. 42(7), 1427–1439 (1997).
[Crossref] [PubMed]

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(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Serranho, P.

Shanmugan, K. S.

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 157–166 (1982).
[Crossref] [PubMed]

Sowa, M. G.

D. P. Popescu, M. D. Hewko, and M. G. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Commun. 269(1), 247–251 (2007).
[Crossref]

Stifter, D.

B. Heise, E. Leiss-Holzinger, M. Pircher, E. Götzinger, B. Baumann, C. K. Hitzenberger, and D. Stifter, “Advanced image processing of retardation scans for polarization-sensitive optical coherence tomography,” in European Conference on Biomedical Optics, (Optical Society of America, 2009), pp. 73720S.

Stiles, J. A.

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 157–166 (1982).
[Crossref] [PubMed]

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(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Strand, T. C.

D. T. Kuan, A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust., Speech, Signal Process. 35(3), 373–383 (1987).
[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(5035), 1178–1181 (1991).
[Crossref] [PubMed]

Sylwestrzak, M.

P. Targowski, M. Iwanicka, L. Tymińska-Widmer, M. Sylwestrzak, and E. A. Kwiatkowska, “Structural examination of easel paintings with optical coherence tomography,” Acc. Chem. Res. 43(6), 826–836 (2010).
[Crossref] [PubMed]

Tao, T.

E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inform. Theory 52(2), 489–509 (2006).
[Crossref]

Targowski, P.

P. Targowski, M. Iwanicka, L. Tymińska-Widmer, M. Sylwestrzak, and E. A. Kwiatkowska, “Structural examination of easel paintings with optical coherence tomography,” Acc. Chem. Res. 43(6), 826–836 (2010).
[Crossref] [PubMed]

Tearney, G. J.

A. E. Desjardins, B. J. Vakoc, W. Y. Oh, S. M. Motaghiannezam, G. J. Tearney, and B. E. Bouma, “Angle-resolved optical coherence tomography with sequential angular selectivity for speckle reduction,” Opt. Express 15(10), 6200–6209 (2007).
[Crossref] [PubMed]

A. Ozcan, A. Bilenca, A. E. Desjardins, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering,” J. Opt. Soc. Am. A 22(7), 1901–1910 (2007).
[Crossref]

N. Iftimia, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography by ‘path length encoded’ angular compounding,” J. Biomed. Opt. 8(2), 260–263 (2003).
[Crossref] [PubMed]

Thrane, L.

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in European Conference on Biomedical Optics, (Optical Society of America, 2007), pp. 66270P.

Tian, H.

H. Tian, H. Cai, J. Lai, and X. Xu, “Effective image noise removal based on difference eigenvalue,” in Proceedings of ICIP 2011 International Conference on Image Processing (Brussels, 2011).
[Crossref]

Tornow, R. P.

Tyminska-Widmer, L.

P. Targowski, M. Iwanicka, L. Tymińska-Widmer, M. Sylwestrzak, and E. A. Kwiatkowska, “Structural examination of easel paintings with optical coherence tomography,” Acc. Chem. Res. 43(6), 826–836 (2010).
[Crossref] [PubMed]

Ulaby, F. T.

F. T. Ulaby, F. Kouyate, B. Brisco, and T. H. L. Williams, “Textural information in SAR images,” IEEE Trans. Geosci. Remote Sensing 24(2), 235–245 (1986).
[Crossref]

Vakoc, B. J.

Vitkin, I. A.

Wagner, M.

Williams, T. H. L.

F. T. Ulaby, F. Kouyate, B. Brisco, and T. H. L. Williams, “Textural information in SAR images,” IEEE Trans. Geosci. Remote Sensing 24(2), 235–245 (1986).
[Crossref]

Wilson, D. L.

Wojtkowski, M.

Xu, X.

H. Tian, H. Cai, J. Lai, and X. Xu, “Effective image noise removal based on difference eigenvalue,” in Proceedings of ICIP 2011 International Conference on Image Processing (Brussels, 2011).
[Crossref]

Yao, M.

Yu, Y.

Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. 11(11), 1260–1270 (2002).
[Crossref]

Zhang, H.

Acc. Chem. Res. (1)

P. Targowski, M. Iwanicka, L. Tymińska-Widmer, M. Sylwestrzak, and E. A. Kwiatkowska, “Structural examination of easel paintings with optical coherence tomography,” Acc. Chem. Res. 43(6), 826–836 (2010).
[Crossref] [PubMed]

Appl. Opt. (2)

Biomed. Opt. Express (1)

IEEE Trans. Acoust., Speech, Signal Process. (1)

D. T. Kuan, A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Trans. Acoust., Speech, Signal Process. 35(3), 373–383 (1987).
[Crossref]

IEEE Trans. Geosci. Remote Sensing (1)

F. T. Ulaby, F. Kouyate, B. Brisco, and T. H. L. Williams, “Textural information in SAR images,” IEEE Trans. Geosci. Remote Sensing 24(2), 235–245 (1986).
[Crossref]

IEEE Trans. Image Process. (3)

Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process. 11(11), 1260–1270 (2002).
[Crossref]

S. Aja-Fernández and C. Alberola-López, “On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering,” IEEE Trans. Image Process. 15(9), 2694–2701 (2006).
[Crossref] [PubMed]

F. Sattar, L. Floreby, G. Salomonsson, and B. Lovstrom, “Image enhancement based on a nonlinear multiscale method,” IEEE Trans. Image Process. 6(6), 888–895 (1997).
[Crossref] [PubMed]

IEEE Trans. Inform. Theory (1)

E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inform. Theory 52(2), 489–509 (2006).
[Crossref]

IEEE Trans. Med. Imag. (1)

A. Pižurica, W. Philips, I. Lemahieu, and M. Acheroy, “A versatile wavelet domain noise filtration technique for medical imaging,” IEEE Trans. Med. Imag. 22(3), 323–331 (2003).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 157–166 (1982).
[Crossref] [PubMed]

D. Geman and S. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984).
[Crossref] [PubMed]

J. Amer. Statist. Assoc. (1)

D. L. Donoho and M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” J. Amer. Statist. Assoc. 90(432), 1200–1224 (1995).
[Crossref]

J. Biomed. Opt. (1)

N. Iftimia, B. E. Bouma, and G. J. Tearney, “Speckle reduction in optical coherence tomography by ‘path length encoded’ angular compounding,” J. Biomed. Opt. 8(2), 260–263 (2003).
[Crossref] [PubMed]

J. Opt. Soc. Am. A (3)

Opt. Commun. (2)

D. P. Popescu, M. D. Hewko, and M. G. Sowa, “Speckle noise attenuation in optical coherence tomography by compounding images acquired at different positions of the sample,” Opt. Commun. 269(1), 247–251 (2007).
[Crossref]

A. F. Fercher, C. K. Hitzenberger, G. Kamp, and S. Y. El-Zaiat, “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117(1), 43–48 (1995).
[Crossref]

Opt. Eng. (1)

J. S. Lee, “Speckle suppression and analysis for synthetic aperture radar images,” Opt. Eng. 25(5), 636–643 (1986).
[Crossref]

Opt. Express (5)

Opt. Lett. (3)

Phys. Med. Biol. (1)

J. M. Schmitt, “Array detection for speckle reduction in optical coherence microscopy,” Phys. Med. Biol. 42(7), 1427–1439 (1997).
[Crossref] [PubMed]

Physica D (1)

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60(1), 259–268 (1992).
[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 J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

SIAM J. Appl. Math. (1)

G. Aubert and J. F. Aujol, “A variational approach to removing multiplicative noise,” SIAM J. Appl. Math. 68(4), 925–946 (2008).
[Crossref]

Other (8)

J. R. Goodman and R. L. Haupt, Statistical Optics (John Wiley & Sons, 2015).

H. Tian, H. Cai, J. Lai, and X. Xu, “Effective image noise removal based on difference eigenvalue,” in Proceedings of ICIP 2011 International Conference on Image Processing (Brussels, 2011).
[Crossref]

T. M. Jørgensen, L. Thrane, M. Mogensen, F. Pedersen, and P. E. Andersen, “Speckle reduction in optical coherence tomography images of human skin by a spatial diversity method,” in European Conference on Biomedical Optics, (Optical Society of America, 2007), pp. 66270P.

J. W. Goodman, “Statistical properties of laser speckle patterns,” in Laser Speckle and Related Phenomena, J. C. Dainty, ed. (Springer, 1975), pp. 9–75.

Y. Meyer, “Oscillating patterns in image processing and nonlinear evolution equations,” in the fifteenth Dean Jacqueline B. Lewis memorial lectures (Vol. 22) (American Mathematical Society, 2001).

G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Springer Science & Business Media, 2006).

L. Rudin, P. L. Lions, and S. Osher, “Multiplicative denoising and deblurring: theory and algorithms,” in Geometric Level Set Methods in Imaging, Vision, and Graphics, S. Osher and N. Paragios, eds. (Springer, 2003), pp. 103–119.
[Crossref]

B. Heise, E. Leiss-Holzinger, M. Pircher, E. Götzinger, B. Baumann, C. K. Hitzenberger, and D. Stifter, “Advanced image processing of retardation scans for polarization-sensitive optical coherence tomography,” in European Conference on Biomedical Optics, (Optical Society of America, 2009), pp. 73720S.

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Figures (8)

Fig. 1:
Fig. 1: Images used in simulations: (a) Lenna and (b) OCT image of retina.
Fig. 2:
Fig. 2: The whole image of Fig. 1(a), its speckled version and different recovered versions with 7 algorithms.
Fig. 3:
Fig. 3: Enlarged ROI #1 of Fig. 1(a), its speckled version and different recovered versions with 7 algorithms.
Fig. 4:
Fig. 4: Enlarged ROI #2 of Fig. 1(a), its speckled version and different recovered versions with 7 algorithms.
Fig. 5:
Fig. 5: The whole image of Fig. 1(b) and its different recovered versions with 7 algorithms.
Fig. 6:
Fig. 6: Enlarged ROI #1 of Fig. 1(b), and its different recovered versions with 7 algorithms.
Fig. 7:
Fig. 7: Enlarged ROI #2 of Fig. 1(b), and its different recovered versions with 7 algorithms.
Fig. 8:
Fig. 8: OCT device flow diagram.

Tables (6)

Tables Icon

Table 1: Different metrics for the images in Fig. 2.

Tables Icon

Table 2: Different metrics for the images in Fig. 3.

Tables Icon

Table 3: Different metrics for the images in Fig. 4.

Tables Icon

Table 4: Different metrics for the images in Fig. 5.

Tables Icon

Table 5: Different metrics for the images in Fig. 6.

Tables Icon

Table 6: Different metrics for the images in Fig. 7.

Equations (15)

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

i ( s ) = o ( s ) n ( s )
f N ( n ) = L L Γ ( L ) n L 1 exp ( L n ) , n 0
f N ( n ) = exp ( n ) , n 0
J ( o ) = S ( log o + i o ) + λ S o
𝒥 ( o ) = S ( log o + i o ) + λ ( s ) S Φ ( o )
o ( k + 1 ) o ( k ) δ = 1 o ( k ) i o ( k ) 2 λ ( s ) div ( Φ ( o ( k ) ) o ( k ) o ( k ) )
o ( k + 1 ) = o ( k ) + δ ( 1 o ( k ) i o ( k ) 2 λ ( s ) div ( Φ ( o ( k ) ) o ( k ) o ( k ) ) )
Φ ( 0 ) = 0
lim x 0 + Φ ( x ) x = lim x 0 + Φ ( x ) = Φ ( 0 ) > 0
lim x + Φ ( x ) x = lim x + Φ ( x ) = 0
SNR = 10 log 10 ( μ R 2 / σ R 2 )
ENL = μ R 2 σ R 2
CNR = μ R μ B σ R 2 + σ B 2
RMSE = o ^ o 2 o 2
η = ( 2 i 2 i ¯ ) ( 2 o ^ 2 o ^ ¯ ) ( 2 i 2 i ¯ ) 2 ( 2 o ^ 2 o ^ ¯ ) 2

Metrics