D. Geman and C. Yang, Nonlinear image recovery with half-quadratic regularization, IEEE Transactions on Image Processing, vol.4, issue.7, pp.932-946, 1995.
DOI : 10.1109/83.392335
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.7349

G. Demoment, Image reconstruction and restoration: overview of common estimation structures and problems, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.37, issue.12, pp.2024-2036, 1989.
DOI : 10.1109/29.45551

L. I. Rudin, S. Osher, and E. 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

I. Daubechies, M. Defrise, and C. Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Communications on Pure and Applied Mathematics, vol.58, issue.11, pp.1413-1457, 2004.
DOI : 10.1002/cpa.20042

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

M. A. Figueiredo and R. D. Nowak, An EM algorithm for wavelet-based image restoration, IEEE Transactions on Image Processing, vol.12, issue.8, pp.906-916, 2003.
DOI : 10.1109/TIP.2003.814255
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.4419

J. Bect, L. Blanc-féraud, G. Aubert, and A. Chambolle, A l 1-Unified Variational Framework for Image Restoration, Proc. European Conference on Computer Vision (ECCV), volume LNCS 3024, pp.1-13, 2004.
DOI : 10.1007/978-3-540-24673-2_1
URL : https://hal.archives-ouvertes.fr/hal-00217251

C. Vonesch, S. Ramani, and M. Unser, Recursive risk estimation for non-linear image deconvolution with a wavelet-domain sparsity constrain, Proc. of the 2008 IEEE International Conf. on Image Proc. (ICIP'08), pp.665-668, 2008.

C. Chaux, P. L. Combettes, J. Pesquet, and V. R. Wajs, A variational formulation for frame-based inverse problems, Inverse Problems, vol.23, issue.4, pp.1495-1518, 2007.
DOI : 10.1088/0266-5611/23/4/008
URL : https://hal.archives-ouvertes.fr/hal-00621883

S. Mallat, A wavelet tour of signal processing, 1998.

L. Younes, Parametric inference for imperfectly observed Gibbsian fields. Probability Theory and Related Fields, pp.625-645, 1989.
DOI : 10.1007/bf00341287

A. Jalobeanu, L. Blanc-féraud, and J. Zerubia, Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method, Pattern Recognition, vol.35, issue.2, pp.341-352, 2002.
DOI : 10.1016/S0031-3203(00)00178-3
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.28.4184

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, pp.391-429, 1977.

M. Jansen and A. Bultheel, Multiple wavelet threshold estimation by generalized cross validation for images with correlated noise, IEEE Transactions on Image Processing, vol.8, issue.7, pp.947-953, 1999.
DOI : 10.1109/83.772237
URL : https://lirias.kuleuven.be/bitstream/123456789/124585/1/corgcv.pdf

M. M. Ichir and A. Mohammad-djafari, Hidden Markov models for wavelet-based blind source separation, IEEE Transactions on Image Processing, vol.15, issue.7, pp.1887-1899, 2006.
DOI : 10.1109/TIP.2006.877068
URL : https://hal.archives-ouvertes.fr/hal-00449843

D. Leporini and J. Pesquet, Bayesian wavelet denoising: Besov priors and non-Gaussian noises, Signal Processing, vol.81, issue.1, pp.55-67, 2001.
DOI : 10.1016/S0165-1684(00)00190-0
URL : https://hal.archives-ouvertes.fr/hal-00621733

J. Gauthier, L. Duval, and J. C. Pesquet, Optimization of Synthesis Oversampled Complex Filter Banks, IEEE Transactions on Signal Processing, vol.57, issue.10, pp.3827-3843, 2009.
DOI : 10.1109/TSP.2009.2023947
URL : https://hal.archives-ouvertes.fr/hal-00405293