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Efficient reduction of speckle noise in optical coherence tomography. Opt. Express 20,
1337–1359 (2012)
5. M. Bashkansky, J. Reintjes, Statistics and reduction of speckle in optical coherence tomography. Opt. Lett. 25, 545–547 (2000)
6. D.D. Duncan, S.J. Kirkpatrick, R.K. Wang, Statistics of local speckle contrast. JOSA A 25,
9–15 (2008)
7. J.W. Goodman, Some fundamental properties of speckle. JOSA 66, 1145–1150 (1976)
8. B. Karamata, K. Hassler, M. Laubscher, T. Lasser, Speckle statistics in optical coherence
tomography. JOSA A 22, 593–596 (2005)
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Opt. 4, 95–105 (1999)
10. N.M. Grzywacz, J. De Juan, C. Ferrone, D. Giannini, D. Huang, G. Koch et al., Statistics
of optical coherence tomography data from human retina. IEEE Trans. Med. Imaging 29,
1224–1237 (2010)
11. D.H. Ross, Finding Bands in Optical Coherence Tomography Images using Curve and Function Fitting (The University of Alabama at Birmingham, 2014)
12. A.F. Fercher, W. Drexler, C.K. Hitzenberger, T. Lasser, Optical coherence tomographyprinciples and applications. Rep. Prog. Phys. 66, 239 (2003)
13. N. George, C. Christensen, J. Bennett, B. Guenther, Speckle noise in displays. JOSA 66,
1282–1290 (1976)
14. J.W. Goodman, Some effects of target-induced scintillation on optical radar performance.
Proc. IEEE 53, 1688–1700 (1965)
15. R. Loudon, The Quantum Theory of Light (OUP, Oxford, 2000)
16. Z. Amini, H. Rabbani, Classification of medical image modeling methods: a review. Curr.
Med. Imaging Rev. 12, 130–148 (2016)
17. I. Jolliffe, Principal Component Analysis (Wiley Online Library, New York, 2002)
18. A. Jung, An introduction to a new data analysis tool: Independent component analysis, in
Proceedings of Workshop GK” Nonlinearity”-Regensburg (2001)
19. A. Hyvärinen, E. Oja, Independent component analysis: algorithms and applications. Neural
Networks 13, 411–430 (2000)
20. M.P. Arakeri, G.R.M. Reddy, A comparative performance evaluation of independent component analysis in medical image denoising, in 2011 International Conference on Recent Trends
in Information Technology (ICRTIT) (2011), pp. 770–774
21. I. Tosic, P. Frossard, Dictionary learning. Sig. Process. Mag. IEEE 28, 27–38 (2011)
22. R. Kafieh, H. Rabbani, I. Selesnik, Three dimensional data-driven multi scale atomic representation of optical coherence tomography. IEEE Trans. Med. Imaging 34, 1042–1062 (2015)
23. I. Daubechies, Ten Lectures on Wavelets, vol. 61 (SIAM, Philadelphia, PA, 1992)
24. T.F. Chan, J.J. Shen, Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods (SIAM, Philadelphia, PA, 2005)
25. M. Jain, S. Sharma, R.M. Sairam, Effect of blur and noise on image denoising based on
PDE, in International Journal of Advanced Computer Research (IJACR), vol. 3(1) Issue-8
March-2013 (2013)
26. H.M. Salinas, 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)
27. M.K. Garvin, M.D. Abràmoff, R. Kardon, S.R. Russell, X. Wu, M. Sonka, Intraretinal layer
segmentation of macular optical coherence tomography images using optimal 3-D graph
search. IEEE Trans. Med. Imaging 27, 1495–1505 (2008)
28. L.D. Cohen, I. Cohen, Finite-element methods for active contour models and balloons for
2-D and 3-D images. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1131–1147 (1993)
29. Y. Yu, S. Zhang, K. Li, D. Metaxas, L. Axel, Deformable models with sparsity constraints for
cardiac motion analysis. Med. Image Anal. 18, 927–937 (2014)
Z. Amini et al.
4. M. Szkulmowski, I. Gorczynska, D. Szlag, M. Sylwestrzak, A. Kowalczyk, M. Wojtkowski,
Efficient reduction of speckle noise in optical coherence tomography. Opt. Express 20,
1337–1359 (2012)
5. M. Bashkansky, J. Reintjes, Statistics and reduction of speckle in optical coherence tomography. Opt. Lett. 25, 545–547 (2000)
6. D.D. Duncan, S.J. Kirkpatrick, R.K. Wang, Statistics of local speckle contrast. JOSA A 25,
9–15 (2008)
7. J.W. Goodman, Some fundamental properties of speckle. JOSA 66, 1145–1150 (1976)
8. B. Karamata, K. Hassler, M. Laubscher, T. Lasser, Speckle statistics in optical coherence
tomography. JOSA A 22, 593–596 (2005)
9. J.M. Schmitt, S. Xiang, K.M. Yung, Speckle in optical coherence tomography. J. Biomed.
Opt. 4, 95–105 (1999)
10. N.M. Grzywacz, J. De Juan, C. Ferrone, D. Giannini, D. Huang, G. Koch et al., Statistics
of optical coherence tomography data from human retina. IEEE Trans. Med. Imaging 29,
1224–1237 (2010)
11. D.H. Ross, Finding Bands in Optical Coherence Tomography Images using Curve and Function Fitting (The University of Alabama at Birmingham, 2014)
12. A.F. Fercher, W. Drexler, C.K. Hitzenberger, T. Lasser, Optical coherence tomographyprinciples and applications. Rep. Prog. Phys. 66, 239 (2003)
13. N. George, C. Christensen, J. Bennett, B. Guenther, Speckle noise in displays. JOSA 66,
1282–1290 (1976)
14. J.W. Goodman, Some effects of target-induced scintillation on optical radar performance.
Proc. IEEE 53, 1688–1700 (1965)
15. R. Loudon, The Quantum Theory of Light (OUP, Oxford, 2000)
16. Z. Amini, H. Rabbani, Classification of medical image modeling methods: a review. Curr.
Med. Imaging Rev. 12, 130–148 (2016)
17. I. Jolliffe, Principal Component Analysis (Wiley Online Library, New York, 2002)
18. A. Jung, An introduction to a new data analysis tool: Independent component analysis, in
Proceedings of Workshop GK” Nonlinearity”-Regensburg (2001)
19. A. Hyvärinen, E. Oja, Independent component analysis: algorithms and applications. Neural
Networks 13, 411–430 (2000)
20. M.P. Arakeri, G.R.M. Reddy, A comparative performance evaluation of independent component analysis in medical image denoising, in 2011 International Conference on Recent Trends
in Information Technology (ICRTIT) (2011), pp. 770–774
21. I. Tosic, P. Frossard, Dictionary learning. Sig. Process. Mag. IEEE 28, 27–38 (2011)
22. R. Kafieh, H. Rabbani, I. Selesnik, Three dimensional data-driven multi scale atomic representation of optical coherence tomography. IEEE Trans. Med. Imaging 34, 1042–1062 (2015)
23. I. Daubechies, Ten Lectures on Wavelets, vol. 61 (SIAM, Philadelphia, PA, 1992)
24. T.F. Chan, J.J. Shen, Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods (SIAM, Philadelphia, PA, 2005)
25. M. Jain, S. Sharma, R.M. Sairam, Effect of blur and noise on image denoising based on
PDE, in International Journal of Advanced Computer Research (IJACR), vol. 3(1) Issue-8
March-2013 (2013)
26. H.M. Salinas, 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)
27. M.K. Garvin, M.D. Abràmoff, R. Kardon, S.R. Russell, X. Wu, M. Sonka, Intraretinal layer
segmentation of macular optical coherence tomography images using optimal 3-D graph
search. IEEE Trans. Med. Imaging 27, 1495–1505 (2008)
28. L.D. Cohen, I. Cohen, Finite-element methods for active contour models and balloons for
2-D and 3-D images. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1131–1147 (1993)
29. Y. Yu, S. Zhang, K. Li, D. Metaxas, L. Axel, Deformable models with sparsity constraints for
cardiac motion analysis. Med. Image Anal. 18, 927–937 (2014)