A. Achim, P. Tsakalides, and A. Bezerianos, SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling, IEEE Transactions on Geoscience and Remote Sensing, vol.41, issue.8, pp.411773-1784, 2003.
DOI : 10.1109/TGRS.2003.813488

G. Aubert and J. Aujol, A variational approach to remove multiplicative noise, J. on Applied Mathematics, vol.68, issue.4, pp.925-946, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00260496

G. Aubert and P. Kornprobst, Mathematical problems in image processing, 2006.

J. Aujol, Some algorithms for total variation based image restoration, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00260494

E. J. Candès and F. Guo, New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction, Signal Processing, vol.82, issue.11, 2002.
DOI : 10.1016/S0165-1684(02)00300-6

A. Chambolle, An algorithm for total variation minimization and application, J. of Mathematical Imaging and Vision, vol.20, issue.1, 2004.

A. Chambolle, Total Variation Minimization and a Class of Binary MRF Models, LNCS, vol.3757, pp.136-152, 2005.
DOI : 10.1007/11585978_10

C. Chesneau, J. Fadili, and J. Starck, Stein block thresholding for image denoising, Applied and Computational Harmonic Analysis, vol.28, issue.1, 2008.
DOI : 10.1016/j.acha.2009.07.003
URL : https://hal.archives-ouvertes.fr/hal-00323319

R. R. Coifman and A. Sowa, Combining the Calculus of Variations and Wavelets for Image Enhancement, Applied and Computational Harmonic Analysis, vol.9, issue.1, 2000.
DOI : 10.1006/acha.2000.0299

P. L. Combettes, Solving monotone inclusions via compositions of nonexpansive averaged operators. Optimization, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00017830

P. L. Combettes and J. Pesquet, A Douglas???Rachford Splitting Approach to Nonsmooth Convex Variational Signal Recovery, IEEE Journal of Selected Topics in Signal Processing, vol.1, issue.4, pp.564-574, 2007.
DOI : 10.1109/JSTSP.2007.910264
URL : https://hal.archives-ouvertes.fr/hal-00621820

D. L. Donoho and I. M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika, vol.81, issue.3, pp.425-455, 1994.
DOI : 10.1093/biomet/81.3.425

S. Durand and M. Nikolova, Denoising of Frame Coefficients Using $\ell^1$ Data-Fidelity Term and Edge-Preserving Regularization, Multiscale Modeling & Simulation, vol.6, issue.2, pp.547-576, 2007.
DOI : 10.1137/06065828X

S. Durand and J. Froment, Reconstruction of Wavelet Coefficients Using Total Variation Minimization, SIAM Journal on Scientific Computing, vol.24, issue.5, pp.1754-1767, 2003.
DOI : 10.1137/S1064827501397792
URL : https://hal.archives-ouvertes.fr/hal-00712152

S. Durand, J. Fadili, and M. Nikolova, Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients, Journal of Mathematical Imaging and Vision, vol.11, issue.11, 2008.
DOI : 10.1007/s10851-009-0180-z
URL : https://hal.archives-ouvertes.fr/hal-00345119

S. Fukuda and H. Hirosawa, Suppression of speckle in synthetic aperture radar images using wavelet, International Journal of Remote Sensing, vol.19, issue.3, pp.507-519, 1998.
DOI : 10.1080/014311698216125

K. Krissian, C. Westin, R. Kikinis, and K. G. Vosburgh, Oriented Speckle Reducing Anisotropic Diffusion, IEEE Transactions on Image Processing, vol.16, issue.5, pp.1412-1424, 2007.
DOI : 10.1109/TIP.2007.891803

J. Ma and G. Plonka, Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion, IEEE Transactions on Image Processing, vol.16, issue.9, pp.2198-2206, 2007.
DOI : 10.1109/TIP.2007.902333

F. Malgouyres, Mathematical analysis of a model which combines total variation and wavelet for image restoration, J. of information processes, vol.2, issue.1, pp.1-10, 2002.

J. Moreau, Fonctions convexes duales et points proximaux dans un espace hilbertien

M. Nikolova, Minimizers of Cost-Functions Involving Nonsmooth Data-Fidelity Terms. Application to the Processing of Outliers, SIAM Journal on Numerical Analysis, vol.40, issue.3, pp.965-994, 2002.
DOI : 10.1137/S0036142901389165

M. Nikolova, Weakly Constrained Minimization: Application to the Estimation of Images and Signals Involving Constant Regions, Journal of Mathematical Imaging and Vision, vol.21, issue.2, pp.155-175, 2004.
DOI : 10.1023/B:JMIV.0000035180.40477.bd

A. Pizurica, A. M. Wink, E. Vansteenkiste, W. Philips, and J. B. Roerdink, A Review of Wavelet Denoising in MRI and Ultrasound Brain Imaging, Current Medical Imaging Reviews, vol.2, issue.2, pp.247-260, 2006.
DOI : 10.2174/157340506776930665

L. Rudin, P. Lions, and S. Osher, Multiplicative Denoising and Deblurring: Theory and Algorithms, Osher and N. Paragios, pp.103-119, 2003.
DOI : 10.1007/0-387-21810-6_6

L. Rudin, S. Osher, and C. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, pp.259-268, 1992.
DOI : 10.1016/0167-2789(92)90242-F

J. Shi and S. Osher, A nonlinear inverse scale space method for a convex mutiplicative noise model, 2007.

F. Ulaby and M. C. Dobson, Handbook of Radar Scattering Statistics for Terrain, 1989.

M. Walessa and M. Datcu, Model-based despeckling and information extraction from SAR images, IEEE Transactions on Geoscience and Remote Sensing, vol.38, issue.5, pp.2258-2269, 2000.
DOI : 10.1109/36.868883

M. Welk, G. Steidl, and J. Weickert, Locally analytic schemes: A link between diffusion filtering and wavelet shrinkage, Applied and Computational Harmonic Analysis, vol.24, issue.2, pp.195-224, 2008.
DOI : 10.1016/j.acha.2007.05.004

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, issue.10, pp.402196-2212, 2002.

Y. Yu and S. T. Acton, Speckle reducing anisotropic diffusion, IEEE Trans. on Image Processing, vol.11, issue.11, pp.1260-1270, 2002.