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53. J. Zhou, A.L. Cunha, M.N. Do, Nonsubsampled contourlet transform: construction and application in enhancement, in IEEE International Conference on Image Processing 2005 (2005),
pp. I-469–72
54. L. Fang, S. Li, Q. Nie, J.A. Izatt, C.A. Toth, S. Farsiu, Sparsity based denoising of spectral
domain optical coherence tomography images. Biomed. Opt. Express 3, 927–942 (2012)
55. P. Chatterjee, P. Milanfar, Clustering-based denoising with locally learned dictionaries. IEEE
Trans. Image Process. 18, 1438–1451 (2009)
56. M. Aharon, M. Elad, A. Bruckstein, k -SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54, 4311–4322 (2006)
57. M. Elad, M. Aharon, Image denoising via sparse and redundant representations over learned
dictionaries. IEEE Trans. Image Process. 15, 3736–3745 (2006)
58. Y.C. Pati, R. Rezaiifar, P.S. Krishnaprasad, Orthogonal matching pursuit: recursive function
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pp. 40–44
59. N. Kingsbury, Complex wavelets for shift invariant analysis and filtering of signals. Appl.
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60. I.W. Selesnick, R.G. Baraniuk, N.C. Kingsbury, The dual-tree complex wavelet transform.
Sig. Process. Mag. IEEE 22, 123–151 (2005)
61. I.W. Selesnick, K.Y. Li, Video denoising using 2D and 3D dual-tree complex wavelet transforms. Wavelets Appl. Sign. Image Process. X 5207, 607–618 (2003)
62. P. Puvanathasan, K. Bizheva, Interval type-II fuzzy anisotropic diffusion algorithm for speckle
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63. D.C. Adler, T.H. Ko, J.G. Fujimoto, Speckle reduction in optical coherence tomography
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64. V. Zlokolica, L. Jovanov, A. Pizurica, P. De Keyser, F. Dhaenens, W. Philips, Wavelet-based
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65. Z. Jian, L. Yu, B. Rao, B.J. Tromberg, Z. Chen, Three-dimensional speckle suppression in
optical coherence tomography based on the curvelet transform. Opt. Express 18, 1024–1032
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66. S. Gupta, R.C. Chauhan, S.C. Saxena, Robust non-homomorphic approach for speckle reduction in medical ultrasound images. Med. Biol. Eng. Compu. 43(2), 189–195 (2005)
67. S. Gupta, L. Kaur, R.C. Chauhan, S.C. Saxena, A versatile technique for visual enhancement
of medical ultrasound images. Digit. Signal Proc. 17(3), 542–560 (2007)
68. S. Yan, J. Yuan, M. Liu, C. Hou, Speckle noise reduction of ultrasound images based on
an undecimated wavelet packet transform domain nonhomomorphic filtering, in Proceedings
of the 2nd International Conference on Biomedical Engineering and Informatics (October
2009), pp. 1–5
69. H. Rabbani, M. Sonka, M.D. Abramoff, Optical coherence tomography noise reduction using
anisotropic local bivariate gaussian mixture prior in 3-D complex wavelet domain. Int. J.
Biomed. Imaging 2013, Article ID 417491, 23 p (2013)
70. V. Katkovnik, K. Egiazarian, J. Astola, Adaptive window size image de-noising based on
intersection of confidence intervals (ICI) rule. J. Math. Imaging Vis. 16(3), 223–235 (2002)
71. A. Ozcan, A. Bilenca, A.E. Desjardins, B.E. Bouma, G.J. Tearney, Speckle reduction in optical
coherence tomography images using digital filtering. JOSA A 24, 1901–1910 (2007)
72. G.A. Campbell, R.M. Foster, Fourier Integrals for Practical Applications (Bell telephone
laboratories, New York, 1948)
73. N. Ahmed, T. Natarajan, K.R. Rao, Discrete cosine transform. IEEE Trans. Comput. 100,
90–93 (1974)
74. R.R. Coifman, M.V. Wickerhauser, Entropy-based algorithms for best basis selection. IEEE
Trans. Inf. Theory 38, 713–718 (1992)
75. D.L. Donoho, Wedgelets: nearly minimax estimation of edges. Ann. Stat. 27, 859–897 (1999)
Z. Amini et al.
52. S.S. Agaian, B. Silver, K.A. Panetta, Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE Trans. Image Process. 16, 741–758 (2007)
53. J. Zhou, A.L. Cunha, M.N. Do, Nonsubsampled contourlet transform: construction and application in enhancement, in IEEE International Conference on Image Processing 2005 (2005),
pp. I-469–72
54. L. Fang, S. Li, Q. Nie, J.A. Izatt, C.A. Toth, S. Farsiu, Sparsity based denoising of spectral
domain optical coherence tomography images. Biomed. Opt. Express 3, 927–942 (2012)
55. P. Chatterjee, P. Milanfar, Clustering-based denoising with locally learned dictionaries. IEEE
Trans. Image Process. 18, 1438–1451 (2009)
56. M. Aharon, M. Elad, A. Bruckstein, k -SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54, 4311–4322 (2006)
57. M. Elad, M. Aharon, Image denoising via sparse and redundant representations over learned
dictionaries. IEEE Trans. Image Process. 15, 3736–3745 (2006)
58. Y.C. Pati, R. Rezaiifar, P.S. Krishnaprasad, Orthogonal matching pursuit: recursive function
approximation with applications to wavelet decomposition, in 1993 Conference Record of
the Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, vol.1 (1993),
pp. 40–44
59. N. Kingsbury, Complex wavelets for shift invariant analysis and filtering of signals. Appl.
Comput. Harmonic Anal. 10, 234–253 (2001)
60. I.W. Selesnick, R.G. Baraniuk, N.C. Kingsbury, The dual-tree complex wavelet transform.
Sig. Process. Mag. IEEE 22, 123–151 (2005)
61. I.W. Selesnick, K.Y. Li, Video denoising using 2D and 3D dual-tree complex wavelet transforms. Wavelets Appl. Sign. Image Process. X 5207, 607–618 (2003)
62. P. Puvanathasan, K. Bizheva, Interval type-II fuzzy anisotropic diffusion algorithm for speckle
noise reduction in optical coherence tomography images. Opt. Express 17, 733–746 (2009)
63. D.C. Adler, T.H. Ko, J.G. Fujimoto, Speckle reduction in optical coherence tomography
images by use of a spatially adaptive wavelet filter. Opt. Lett. 29, 2878–2880 (2004)
64. V. Zlokolica, L. Jovanov, A. Pizurica, P. De Keyser, F. Dhaenens, W. Philips, Wavelet-based
denoising for 3D OCT images, in Proceedings of SPIE (2007), p. 66960P
65. Z. Jian, L. Yu, B. Rao, B.J. Tromberg, Z. Chen, Three-dimensional speckle suppression in
optical coherence tomography based on the curvelet transform. Opt. Express 18, 1024–1032
(2010)
66. S. Gupta, R.C. Chauhan, S.C. Saxena, Robust non-homomorphic approach for speckle reduction in medical ultrasound images. Med. Biol. Eng. Compu. 43(2), 189–195 (2005)
67. S. Gupta, L. Kaur, R.C. Chauhan, S.C. Saxena, A versatile technique for visual enhancement
of medical ultrasound images. Digit. Signal Proc. 17(3), 542–560 (2007)
68. S. Yan, J. Yuan, M. Liu, C. Hou, Speckle noise reduction of ultrasound images based on
an undecimated wavelet packet transform domain nonhomomorphic filtering, in Proceedings
of the 2nd International Conference on Biomedical Engineering and Informatics (October
2009), pp. 1–5
69. H. Rabbani, M. Sonka, M.D. Abramoff, Optical coherence tomography noise reduction using
anisotropic local bivariate gaussian mixture prior in 3-D complex wavelet domain. Int. J.
Biomed. Imaging 2013, Article ID 417491, 23 p (2013)
70. V. Katkovnik, K. Egiazarian, J. Astola, Adaptive window size image de-noising based on
intersection of confidence intervals (ICI) rule. J. Math. Imaging Vis. 16(3), 223–235 (2002)
71. A. Ozcan, A. Bilenca, A.E. Desjardins, B.E. Bouma, G.J. Tearney, Speckle reduction in optical
coherence tomography images using digital filtering. JOSA A 24, 1901–1910 (2007)
72. G.A. Campbell, R.M. Foster, Fourier Integrals for Practical Applications (Bell telephone
laboratories, New York, 1948)
73. N. Ahmed, T. Natarajan, K.R. Rao, Discrete cosine transform. IEEE Trans. Comput. 100,
90–93 (1974)
74. R.R. Coifman, M.V. Wickerhauser, Entropy-based algorithms for best basis selection. IEEE
Trans. Inf. Theory 38, 713–718 (1992)
75. D.L. Donoho, Wedgelets: nearly minimax estimation of edges. Ann. Stat. 27, 859–897 (1999)