F. Argenti, A. Lapini, L. Alparone, and T. Bianchi, A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images, IEEE Geoscience and Remote Sensing Magazine, vol.1, issue.3, pp.6-35, 2013.
DOI : 10.1109/MGRS.2013.2277512

M. Lebrun, A. Buades, and J. Morel, A non-local Bayesian Image Denoising Algorithm, SIAM J. Imaging Sciences, vol.6, issue.3, 2013.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering, IEEE Transactions on Image Processing, vol.16, issue.8, 2007.
DOI : 10.1109/TIP.2007.901238

L. Zhang, W. Dong, D. Zhang, and G. Shi, Two-stage image denoising by principal component analysis with local pixel grouping, Pattern Recognition, vol.43, issue.4, pp.1531-1549, 2010.
DOI : 10.1016/j.patcog.2009.09.023

C. Deledalle, J. Salmon, and A. S. Dalalyan, Image denoising with patch based PCA: local versus global, Procedings of the British Machine Vision Conference 2011, 2011.
DOI : 10.5244/C.25.25
URL : https://hal.archives-ouvertes.fr/hal-00654289

M. Aharon, M. Elad, and A. M. Bruckstein, <tex>$rm K$</tex>-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on Signal Processing, vol.54, issue.11, pp.4311-4322, 2006.
DOI : 10.1109/TSP.2006.881199

B. A. Olshausen, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, vol.381, issue.6583, 1996.
DOI : 10.1038/381607a0

G. Yu, G. Sapiro, and S. Mallat, Solving Inverse Problems With Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity, IEEE Transactions on Image Processing, vol.21, pp.2481-2499, 2012.

D. Zoran and Y. Weiss, From learning models of natural image patches to whole image restoration, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126278

N. Jojic, B. J. Frey, and A. Kannan, Epitomic analysis of appearance and shape, Proceedings Ninth IEEE International Conference on Computer Vision, pp.34-41, 2003.
DOI : 10.1109/ICCV.2003.1238311

L. Beno??tbeno??t, J. Mairal, F. Bach, and J. Ponce, Sparse image representation with epitomes, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp.2913-2920, 2011.

J. W. Goodman, Some fundamental properties of speckle*, Journal of the Optical Society of America, vol.66, issue.11, 1976.
DOI : 10.1364/JOSA.66.001145

H. Xie, L. Pierce, and F. T. Ulaby, Statistical properties of logarithmically transformed speckle, IEEE Transactions on Geoscience and Remote Sensing, vol.40, issue.3, pp.721-727, 2002.
DOI : 10.1109/TGRS.2002.1000333

J. M. Bioucas-dias and M. A. Figueiredo, Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization, IEEE Transactions on Image Processing, vol.19, issue.7, 2010.
DOI : 10.1109/TIP.2010.2045029

R. Corless, G. Gonnet, D. Hare, D. Jeffrey, and D. Knuth, On the LambertW function, Advances in Computational Mathematics, vol.1, issue.6, pp.329-359, 1996.
DOI : 10.1007/BF02124750

H. Xie, L. E. Pierce, and F. T. Ulaby, SAR speckle reduction using wavelet denoising and Markov random field modeling, IEEE Transactions on Geoscience and Remote Sensing, vol.40, issue.10, pp.2196-2212, 2002.
DOI : 10.1109/TGRS.2002.802473

C. Deledalle, L. Denis, F. Tupin, A. Reigbers, and M. Jäger, NL-SAR: a unified Non-Local framework for resolution-preserving (Pol)(In)SAR denoising, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00844118

M. Makitalo, A. Foi, D. Fevralev, and V. Lukin, Denoising of single-look SAR images based on variance stabilization and nonlocal filters, 2010 International Conference on Mathematical Methods in Electromagnetic Theory, pp.1-4, 2010.
DOI : 10.1109/MMET.2010.5611418