N. Chakrova, B. Rieger, and S. Stallinga, Deconvolution methods for structured illumination microscopy, Journal of the Optical Society of America A, vol.33, issue.7, pp.1084-7529, 2016.
DOI : 10.1364/JOSAA.33.000B12

J. P. Oliveira, M. A. Figueiredo, and J. M. Bioucas-dias, Parametric Blur Estimation for Blind Restoration of Natural Images: Linear Motion and Out-of-Focus, IEEE Transactions on Image Processing, vol.23, issue.1, pp.466-477, 2014.
DOI : 10.1109/TIP.2013.2286328

E. Vera, M. Vega, R. Molina, and A. K. Katsaggelos, Iterative image restoration using nonstationary priors, Applied Optics, vol.52, issue.10, pp.102-110, 2013.
DOI : 10.1364/AO.52.00D102
URL : http://ivpl.ece.northwestern.edu/system/files/ao-52-10-D102.pdf

S. Bongard, F. Soulez, and ´. E. Thiébaut, 3D deconvolution of hyper-spectral astronomical data, Monthly Notices of the Royal Astronomical Society, vol.52, issue.1, pp.258-270, 2011.
DOI : 10.1093/comjnl/bxn031
URL : https://hal.archives-ouvertes.fr/hal-00618666

F. Orieux, E. Sepulveda, V. Loriette, B. Dubertret, and J. C. Olivo-marin, Bayesian Estimation for Optimized Structured Illumination Microscopy, IEEE Transactions on Image Processing, vol.21, issue.2, pp.601-614, 2012.
DOI : 10.1109/TIP.2011.2162741
URL : https://hal.archives-ouvertes.fr/hal-01225862

J. Giovannelli and A. Coulais, Positive deconvolution for superimposed extended source and point sources, Astronomy & Astrophysics, vol.25, issue.1, pp.401-412, 2005.
DOI : 10.1145/321150.321157
URL : http://www.aanda.org/articles/aa/pdf/2005/31/aa0011-04.pdf

N. Komodakis and J. Pesquet, Playing with Duality: An overview of recent primal?dual approaches for solving large-scale optimization problems, IEEE Signal Processing Magazine, vol.32, issue.6
DOI : 10.1109/MSP.2014.2377273
URL : https://hal.archives-ouvertes.fr/hal-01010437

A. Beck and M. Teboulle, Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems, IEEE Transactions on Image Processing, vol.18, issue.11, pp.2419-2453, 2009.
DOI : 10.1109/TIP.2009.2028250

S. Boyd and L. Vandenberghe, Convex Optimization, 3, pp.487-487, 2010.
DOI : 10.1017/CBO9780511804441

G. Scarpa, R. Gaetano, M. Haindl, and J. Zerubia, Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation, IEEE Transactions on Image Processing, vol.18, issue.8, pp.1830-1843, 2009.
DOI : 10.1109/TIP.2009.2020534
URL : https://hal.archives-ouvertes.fr/inria-00503201

Y. Zheng, A. Fraysse, and T. Rodet, Efficient Variational Bayesian Approximation Method Based on Subspace Optimization, IEEE Transactions on Image Processing, vol.24, issue.2, pp.681-693, 2015.
DOI : 10.1109/TIP.2014.2383321
URL : https://hal.archives-ouvertes.fr/hal-00990003

A. Fraysse and T. Rodet, A Measure-Theoretic Variational Bayesian Algorithm for Large Dimensional Problems, SIAM Journal on Imaging Sciences, vol.7, issue.4, pp.2591-2622, 2014.
DOI : 10.1137/140966575
URL : https://hal.archives-ouvertes.fr/hal-00702259

S. D. Babacan, R. Molina, and A. K. Katsaggelos, Variational Bayesian Super Resolution, IEEE Transactions on Image Processing, vol.20, issue.4, pp.984-999, 2011.
DOI : 10.1109/TIP.2010.2080278
URL : http://decsai.ugr.es/vip/files/journals/2011SR.BMK.pdf

M. Pereyra, P. Schniter, E. Chouzenoux, J. Pesquet, J. Tourneret et al., A Survey of Stochastic Simulation and Optimization Methods in Signal Processing, IEEE Journal of Selected Topics in Signal Processing, vol.10, issue.2, pp.224-241, 2016.
DOI : 10.1109/JSTSP.2015.2496908
URL : https://hal.archives-ouvertes.fr/hal-01312917

X. Descombes, R. Morris, J. Zerubia, and M. Berthod, Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood, IEEE Transactions on Image Processing, vol.8, issue.7, pp.954-963, 1999.
DOI : 10.1109/83.772239
URL : https://hal.archives-ouvertes.fr/inria-00073679

C. Gilavert, S. Moussaoui, and J. Idier, Efficient Gaussian Sampling for Solving Large-Scale Inverse Problems Using MCMC, IEEE Transactions on Signal Processing, vol.63, issue.1, pp.70-80, 2015.
DOI : 10.1109/TSP.2014.2367457
URL : https://hal.archives-ouvertes.fr/hal-01059414

F. Orieux, O. Féron, and J. Giovannelli, Sampling High-Dimensional Gaussian Distributions for General Linear Inverse Problems, IEEE Signal Processing Letters, vol.19, issue.5, pp.251-254, 2012.
DOI : 10.1109/LSP.2012.2189104
URL : https://hal.archives-ouvertes.fr/hal-00779449

L. Chaari, J. Pesquet, J. Tourneret, P. Ciuciu, and A. Benazza-benyahia, A Hierarchical Bayesian Model for Frame Representation, IEEE Transactions on Signal Processing, vol.58, issue.11, pp.5560-5571, 2010.
DOI : 10.1109/TSP.2010.2055562
URL : https://hal.archives-ouvertes.fr/hal-00692261

M. Pereyra, Maximum-a-Posteriori Estimation with Bayesian Confidence Regions, SIAM Journal on Imaging Sciences, vol.10, issue.1, 2016.
DOI : 10.1137/16M1071249

F. Orieux, J. Giovannelli, and T. Rodet, Bayesian estimation of regularization and point spread function parameters for Wiener???Hunt deconvolution, Journal of the Optical Society of America A, vol.27, issue.7, pp.1593-1607, 2010.
DOI : 10.1364/JOSAA.27.001593
URL : https://hal.archives-ouvertes.fr/hal-00674508

J. Giovannelli, Unsupervised Bayesian Convex Deconvolution Based on a Field With an Explicit Partition Function, IEEE Transactions on Image Processing, vol.17, issue.1, pp.16-26, 2008.
DOI : 10.1109/TIP.2007.911819
URL : https://hal.archives-ouvertes.fr/hal-00879198

R. G. Chinchilla, Efficiently Sampling from High-Dimensional Mixture of Gaussians Distributions and Applications to Inverse Problems, 2016.

J. Idier, Convex half-quadratic criteria and interacting auxiliary variables for image restoration, IEEE Transactions on Image Processing, vol.10, issue.7, pp.1001-1009, 2001.
DOI : 10.1109/83.931094

F. Champagnat and J. Idier, A Connection Between Half-Quadratic Criteria and EM Algorithms, IEEE Signal Processing Letters, vol.11, issue.9, pp.709-712, 2004.
DOI : 10.1109/LSP.2004.833511
URL : http://www.irccyn.ec-nantes.fr/~idier/./pub/champagnat03.pdf

M. West, Outlier Models and Prior Distributions in Bayesian Linear Regression, Journal of the Royal Statistical Society Series B Methodological, vol.46, issue.3, pp.431-439, 1984.