[1] I. G. Cumming and F. H. Wong, “Digital Processing of Synthetic Aperture Radar Data,” Norwood, MA: Artech House, 2005.##
[2] J. Goodman, “Speckle Phenomena in Optics: Theory and Applications,” Roberts and Company Publishers, 2007.##
[3] A.Shafiee, E. Yazidian, and M. beheshti, “Speckle noise reduction and SAR image reconstruction using compressed sensing,” Journal of Radar, vol. 4, no. 2, pp. 19-29, 1395. (In Persian)##
[4] A. Schmitt, “Multiscale and Multidirectional Multilooking for SAR Image Enhancement,” IEEE ،Transactions on Geoscience and Remote Sensing, vol. 54, pp. 5117-5134, 2016.##
[5] D. Kuan, A. Sawchuk, T. Strand, and P. Chavel, “Adaptive restoration of images with speckle,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 35, pp. 373-383, 1987.##
[6] J.-S. Lee, “Digital image enhancement and noise filtering by use of local statistics,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 2, pp. 165–168, 1980.##
[7] F. Argenti and L. Alparone, “Speckle removal from SAR images in the undecimated wavelet domain,” IEEE Transactions on Geoscience and Remote Sensing, vol. 40, pp. 2363-2374, 2002.##
[8] H. Xie, L. E. Pierce, and F. T. Ulaby, “SAR speckle reduction using wavelet denoising and markov random field modeling,” IEEE Trans. Geosci. Remote Sensing, vol. 10, pp. 196–2212, 2002.##
[9] G. Aubert and J.-F. Aujol, “A variational approach to removing multiplicative noise,” SIAM J. Appl. Math. vol. 68, pp. 925–946, 2008.##
[10] J. M. Bioucas-Dias and M. A. Figueiredo, “Multiplicative noise removal using variable splitting and constrained optimization,” IEEE Trans. Image Process. vol. 19, no. 10, pp. 1720–1730, 2010.##
[11] J. Shi and S. Osher, “A nonlinear inverse scale space method for a convex multiplicative noise model,” SIAM J. Imaging Sci. vo1. 1, no. 3, pp. 294–321, 2008.##
[12] S. Durand, J. Fadili, and M. Nikolova, “Multiplicative noise removal using l1 fidelity on frame coefficients,” J. Math. Imaging Vision. vol. 36, no. 3, pp. 201–226, 2010.##
[13] N. Karimi, H. Amindavar, R. L. Kirlin and A. Rajabi, “Blind single image super resolution based on compressive sensing,” Journal of Visual Commu. Image Repres, vol. 33, pp. 94-103, 2015.##
[14] C. He, L. Liu, L. Xu, M. Liu, and M. Liao, “Learning Based Compressed Sensing for SAR Image Super-Resolution,” IEEE Journal of Selected Topics in Applied Earth Observ. and Remote Sens., vol. 5, pp. 1272-1281, 2012.##
[15] N. Karimi and M. Taban, “Nonparametric blind SAR image super resolution based on combination of the compressive sensing and sparse priors,” Journal of Visual Commu. Image Repres., vol. 55, pp. 853-865, 2018.##
[16] Jing Dong, Zifa Han, Yuxin Zhao, Wenwu Wang, Ales Prochazka, and Jonathon Chambers, “Sparse analysis model based multiplicative noise removal with enhanced regularization,” Signal Processing, vol. 137, pp. 160-176, 2017.##
[17] Hh Sh. Li, G. Wang, and X. Zhao, “Multiplicative noise removal via adaptive learned dictionaries and TV regularization,” Digital Signal Process., vol. 50, pp. 218-228, 2016.##
[18] Y. Hao , X. Feng, and J. Xu, “Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation,” Signal Process, vol. 92, pp. 1536–1549, 2012.##
[19] Y.-M. Huang, L. Moisan, M. K. Ng, and T. Zeng, “Multiplicative noise removal via a learned dictionary,” IEEE Trans. Image Process, vol. 21, no. 11, pp. 4534–4543, 2012.##
[20] Y. Dong and T. Zeng T, “A convex variational model for restoring blurred images with multiplicative noise,” SIAM J. Imaging Sci., vol. 6, pp. 1598–1625, 2013.##
[21] H. Song, L. Qing, Y. Wu, and X. He, “Adaptive regularization-based space-time super-resolution reconstruction,” Signal. Process. Image Commun., vol. 28, pp. 763–778, 2013.##
[22] F. Argenti, A. Lapini, T. Bianchi, L. Alparone, “A tutorial on speckle reduction in synthetic aperture radar images,” IEEE Geosci. Remote Sens. Mag., vol. 1, no. 3, pp. 6–35, 2013.##
[23] B. Scholkopf, A. Smola, and K.-R. Muller, “Nonlinear component analysis as a kernel eigenvalue problem,” Neural Computation, vol.10, no. 5, pp. 1299-1319, 1998.##
[24] S. Mika, B. Schlkopf, A. Smola, K.-R. Mller, M. Scholz, and G. Rtsch, “Kernel pca and de-noising in feature spaces,” Advances in Neural Information Processing Systems 11, MIT Press, pp. 536-542, 1999.##
[25] S. Boyd , N. Parikh , E. Chu , B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Found. Trends Mach. Learn., vol. 3, no. 1, pp. 1–122, 2011.##
[26] T. Goldstein and S. Osher, “The split Bergman algorithm for l1 regularized problems,” SIAM J. Imaging Sci., vol. 2, pp. 323–343, 2009.##
[27] J. Tropp, “Greed is good: algorithmic results for sparse approximation,” IEEE Trans. Inf. Theory, vol. 50, pp. 2231–2242, 2004.##
[28] M. Aharon, M. Elad, and A. Bruckstein, “K-svd: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process, vol. 54, pp. 4311-4322, 2004.##
[29] Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process, vol. 13, pp. 600-612, 2004.##
)
