M. Bertero and P. Boccacci, Introduction to Inverse Problems in Imaging, 1998.
DOI : 10.1887/0750304359

J. Idier, Bayesian approach to Inverse problems, 2008.
DOI : 10.1002/9780470611197
URL : https://hal.archives-ouvertes.fr/hal-00400668

B. Frieden, Image enhancement and restoration, Picture Processing and Digital Filtering, pp.177-248, 1975.

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

G. T. Gordon, R. , and H. , Reconstruction of pictures from their projections, Communications of the ACM, vol.14, issue.12, pp.759-768, 1971.
DOI : 10.1145/362919.362925

R. M. Lewitt and S. Matej, Overview of methods for image reconstruction from projections in emission computed tomography, Proceedings of the IEE, pp.1588-1611, 2003.
DOI : 10.1109/JPROC.2003.817882

W. Gilks, S. Richardson, and D. Spiegehalter, Markov Chain Monte Carlo in Practice, 1999.

C. Robert, The Bayesian Choice, 2001.
DOI : 10.1007/978-1-4757-4314-2

S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Mach. Intell, vol.6, pp.721-741, 1984.

D. Andrews and C. Mallows, Scale mixtures of normal distributions, J. R. Statist. Soc. B, pp.99-102, 1974.

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

G. Papandreou and A. Yuille, Gaussian sampling by local perturbations, Proceedings of NIPS, 2010.

E. M. Scheuer and D. S. Stoller, On the Generation of Normal Random Vectors, Technometrics, vol.6, issue.3, pp.278-281, 1962.
DOI : 10.1080/00401706.1962.10490011

D. R. Barr and N. L. Slezak, A comparison of multivariate normal generators, Communications of the ACM, vol.15, issue.12, pp.1048-1049, 1972.
DOI : 10.1145/361598.361620

H. Rue, Fast sampling of Gaussian Markov random fields, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.2, pp.325-338, 2001.
DOI : 10.1111/1467-9868.00288

W. F. Trench, An Algorithm for the Inversion of Finite Toeplitz Matrices, Journal of the Society for Industrial and Applied Mathematics, vol.12, issue.3, pp.515-522, 1964.
DOI : 10.1137/0112045

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

P. Lalanne, D. Prévost, and P. Chavel, Stochastic artificial retinas: algorithm, optoelectronic circuits, and implementation, Applied Optics, vol.40, issue.23, pp.3861-3876, 2001.
DOI : 10.1364/AO.40.003861
URL : https://hal.archives-ouvertes.fr/hal-00862122

A. Parker and C. Fox, Sampling Gaussian Distributions in Krylov Spaces with Conjugate Gradients, SIAM Journal on Scientific Computing, vol.34, issue.3, pp.312-334, 2012.
DOI : 10.1137/110831404

E. Aune, J. Eidsvik, and Y. Pokern, Iterative numerical methods for sampling from high dimensional Gaussian distributions, Statistics and Computing, vol.49, issue.2, pp.1-21, 2013.
DOI : 10.1007/s11222-012-9326-8

E. Chow and Y. Saad, Preconditioned Krylov Subspace Methods for Sampling Multivariate Gaussian Distributions, SIAM Journal on Scientific Computing, vol.36, issue.2, pp.588-608, 2014.
DOI : 10.1137/130920587

Y. Amit and U. Grenander, Comparing sweep strategies for stochastic relaxation, Journal of Multivariate Analysis, vol.37, issue.2, pp.197-222, 1991.
DOI : 10.1016/0047-259X(91)90080-L

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, p.251, 2012.
DOI : 10.1109/LSP.2012.2189104
URL : https://hal.archives-ouvertes.fr/hal-00779449

X. Tan, J. Li, and P. Stoica, Efficient sparse Bayesian learning via Gibbs sampling, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.3634-3637, 2010.
DOI : 10.1109/ICASSP.2010.5495896

P. J. Green, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995.
DOI : 10.1093/biomet/82.4.711

R. Waagepetersen and D. Sorensen, A Tutorial on Reversible Jump MCMC with a View toward Applications in QTL-mapping, International Statistical Review, vol.146, issue.1, pp.49-61, 2001.
DOI : 10.2307/2533661

C. Andrieu and J. Thoms, A tutorial on adaptive MCMC, Statistics and Computing, vol.61, issue.3, pp.343-373, 2008.
DOI : 10.1007/s11222-008-9110-y

Y. Atchade, G. Fort, E. Moulines, and P. Priouret, Adaptive Markov chain Monte Carlo: theory and methods, Bayesian Time Series Models, pp.33-53, 2011.
DOI : 10.1017/CBO9780511984679.003

P. De-forcrand, Monte Carlo quasi-heatbath by approximate inversion, Phys. Rev. D, vol.59, issue.3, pp.3698-3701, 1999.

R. Barrett, M. Berry, T. F. Chan, J. Demmel, J. Donato et al., Romine, and H. van der Vorst, Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, 1994.

A. Gelman and D. B. Rubin, Inference from Iterative Simulation Using Multiple Sequences, Statistical Science, vol.7, issue.4, pp.457-472, 1992.
DOI : 10.1214/ss/1177011136

J. S. Liu, Monte Carlo Strategies in Scientific Computing, 2008.
DOI : 10.1007/978-0-387-76371-2

J. Goodman and A. D. Sokal, Multigrid Monte Carlo method. Conceptual foundations, Physical Review D, vol.40, issue.6, 1989.
DOI : 10.1103/PhysRevD.40.2035

B. Bercu and P. Fraysse, A Robbins???Monro procedure for estimation in semiparametric regression models, The Annals of Statistics, vol.40, issue.2, pp.666-693, 2012.
DOI : 10.1214/12-AOS969
URL : https://hal.archives-ouvertes.fr/hal-00551832

C. Andrieu and C. Robert, Controlled MCMC for optimal sampling, Cahiers de Mathématiques du Ceremade, 2001.

A. Benveniste, M. Métivier, and P. Priouret, Adaptive algorithms and stochastic approximations, 2012.
DOI : 10.1007/978-3-642-75894-2

C. Andrieu, E. Moulines, and P. Priouret, Stability of Stochastic Approximation under Verifiable Conditions, SIAM Journal on Control and Optimization, vol.44, issue.1, pp.283-312, 2006.
DOI : 10.1137/S0363012902417267
URL : https://hal.archives-ouvertes.fr/hal-00023475

H. Haario, E. Saksman, and J. Tamminen, An Adaptive Metropolis Algorithm, Bernoulli, vol.7, issue.2, pp.223-242, 2001.
DOI : 10.2307/3318737

Y. F. Atchadé and J. S. Rosenthal, On adaptive Markov chain Monte Carlo algorithms, Bernoulli, vol.11, issue.5, pp.815-828, 2005.
DOI : 10.3150/bj/1130077595

G. O. Roberts, A. Gelman, and W. R. Gilks, Weak convergence and optimal scaling of random walk Metropolis algorithms, The Annals of Applied Probability, vol.7, issue.1, pp.110-120, 1997.
DOI : 10.1214/aoap/1034625254

A. Gelman, G. O. Roberts, and W. R. Gilks, Efficient Metropolis jumping rules, Bayesian statistics 5, pp.599-607, 1996.

Y. F. Achadé, An Adaptive Version for the Metropolis Adjusted Langevin Algorithm with a Truncated Drift, Methodology and Computing in Applied Probability, vol.22, issue.2, pp.235-254, 2006.
DOI : 10.1007/s11009-006-8550-0

G. O. Roberts and J. S. Rosenthal, Optimal scaling of discrete approximations to Langevin diffusions, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.60, issue.1, pp.255-268, 1998.
DOI : 10.1111/1467-9868.00123

S. C. Park, M. K. Park, and M. G. Kang, Super-resolution image reconstruction: a technical overview, IEEE Signal Processing Magazine, vol.20, issue.3, pp.21-36, 2003.
DOI : 10.1109/MSP.2003.1203207

G. Rochefort, F. Champagnat, G. L. Besnerais, and J. Giovannelli, An Improved Observation Model for Super-Resolution Under Affine Motion, IEEE Transactions on Image Processing, vol.15, issue.11, pp.3325-3337, 2006.
DOI : 10.1109/TIP.2006.881996

H. Jeffreys, An Invariant Form for the Prior Probability in Estimation Problems, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.186, issue.1007, pp.453-461, 1946.
DOI : 10.1098/rspa.1946.0056

G. O. Roberts and R. L. Tweedie, Exponential Convergence of Langevin Distributions and Their Discrete Approximations, Bernoulli, vol.2, issue.4, pp.341-363, 1996.
DOI : 10.2307/3318418