M. Tanter and M. Fink, Ultrafast imaging in biomedical ultrasound, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol.61, issue.1, pp.102-119, 2014.
DOI : 10.1109/TUFFC.2014.2882

O. M. Rindal, J. P. Åsen, S. Holm, and A. Austeng, Understanding contrast improvements from capon beamforming, 2014 IEEE International Ultrasonics Symposium, pp.1694-1697, 2014.
DOI : 10.1109/ULTSYM.2014.0420

M. A. Gungor and I. Karagoz, The homogeneity map method for speckle reduction in diagnostic ultrasound images, Measurement, vol.68, pp.100-110, 2015.
DOI : 10.1016/j.measurement.2015.02.047

O. V. Michailovich and A. Tannenbaum, Despeckling of medical ultrasound images, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.53, issue.1, pp.64-78, 2006.
DOI : 10.1109/TUFFC.2006.1588392

J. A. Noble and D. Boukerroui, Ultrasound image segmentation: a survey, IEEE Transactions on Medical Imaging, vol.25, issue.8, pp.987-1010, 2006.
DOI : 10.1109/TMI.2006.877092
URL : https://hal.archives-ouvertes.fr/hal-00338658

M. Pereyra, N. Dobigeon, H. Batatia, and J. Tourneret, Segmentation of Skin Lesions in 2-D and 3-D Ultrasound Images Using a Spatially Coherent Generalized Rayleigh Mixture Model, IEEE Transactions on Medical Imaging, vol.31, issue.8, pp.1509-1520, 2012.
DOI : 10.1109/TMI.2012.2190617

M. Alessandrini, A restoration framework for ultrasonic tissue characterization, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.58, issue.11, pp.2344-2360, 2011.
DOI : 10.1109/TUFFC.2011.2092
URL : https://hal.archives-ouvertes.fr/hal-00688924

T. L. Szabo, Diagnostic Ultrasound Imaging: Inside Out, 2004.

J. Ng, R. Prager, N. Kingsbury, G. Treece, and A. Gee, Wavelet restoration of medical pulse-echo ultrasound images in an EM framework, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.54, issue.3, pp.550-568, 2007.
DOI : 10.1109/TUFFC.2007.278

J. A. Jensen, J. Mathorne, T. Gravesen, and B. Stage, Deconvolution of in vivo ultrasound B-mode images, Ultrason. Imag, vol.15, issue.2, pp.122-133, 1993.

T. Taxt, Restoration of medical ultrasound images using two-dimensional homomorphic deconvolution, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.42, issue.4, pp.543-554, 1995.
DOI : 10.1109/58.393097

O. Michailovich and A. Tannenbaum, Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach, IEEE Transactions on Image Processing, vol.16, issue.12, pp.3005-3019, 2007.
DOI : 10.1109/TIP.2007.910179

O. V. Michailovich and D. Adam, A novel approach to the 2-D blind deconvolution problem in medical ultrasound, IEEE Transactions on Medical Imaging, vol.24, issue.1, pp.86-104, 2005.
DOI : 10.1109/TMI.2004.838326

J. G. Nagy and D. P. Leary, Restoring Images Degraded by Spatially Variant Blur, SIAM Journal on Scientific Computing, vol.19, issue.4, pp.1063-1082, 1998.
DOI : 10.1137/S106482759528507X

R. Jirik and T. Taxt, Two-dimensional blind Bayesian deconvolution of medical ultrasound images, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.55, issue.10, pp.2140-2153, 2008.
DOI : 10.1109/TUFFC.914

C. Yu, C. Zhang, and L. Xie, A blind deconvolution approach to ultrasound imaging, IEEE Trans. Ultrason., Ferroelectr., Freq. Control, vol.59, issue.2, pp.271-280, 2012.

L. Bar, N. Sochen, and N. Kiryati, Variational Pairing of Image Segmentation and Blind Restoration, Computer Vision (Lecture Notes in Computer Science), pp.166-177, 2004.
DOI : 10.1007/978-3-540-24671-8_13

L. Bar, Mumford and Shah Model and its Applications to Image Segmentation andImage Restoration, Handbook of Mathematical Methods in Imaging, pp.1095-1157, 2011.
DOI : 10.1007/978-0-387-92920-0_25

R. Chan, H. Yang, and T. Zeng, A Two-Stage Image Segmentation Method for Blurry Images with Poisson or Multiplicative Gamma Noise, SIAM Journal on Imaging Sciences, vol.7, issue.1, pp.98-127, 2014.
DOI : 10.1137/130920241

H. Ayasso and A. Mohammad-djafari, Joint NDT Image Restoration and Segmentation Using Gauss–Markov–Potts Prior Models and Variational Bayesian Computation, IEEE Transactions on Image Processing, vol.19, issue.9, pp.2265-2277, 2010.
DOI : 10.1109/TIP.2010.2047902

M. Storath, A. Weinmann, J. Frikel, and M. Unser, Joint image reconstruction and segmentation using the Potts model, Inverse Problems, vol.31, issue.2, pp.1-29, 2015.
DOI : 10.1088/0266-5611/31/2/025003

G. Paul, J. Cardinale, and I. F. Sbalzarini, Coupling Image Restoration and Segmentation: A Generalized Linear Model/Bregman Perspective, International Journal of Computer Vision, vol.18, issue.9, pp.69-93, 2013.
DOI : 10.1007/s11263-013-0615-2

M. Mignotte, A segmentation-based regularization term for image deconvolution, IEEE Transactions on Image Processing, vol.15, issue.7, pp.1973-1984, 2006.
DOI : 10.1109/TIP.2006.873446

D. P. Hruska and M. L. Oelze, Improved parameter estimates based on the homodyned K distribution, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.56, issue.11, pp.2471-2481, 2009.
DOI : 10.1109/TUFFC.2009.1334

A. Larrue and J. A. Noble, Nakagami imaging with small windows, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.887-890, 2011.
DOI : 10.1109/ISBI.2011.5872546

O. Bernard, B. Touil, J. , and D. Friboulet, Statistical Modeling of the Radio-Frequency Signal for Partially- and Fully-Developed Speckle Based on a Generalized Gaussian Model with Application to Echocardiography, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol.54, issue.10, pp.2189-2194, 2007.
DOI : 10.1109/TUFFC.2007.515

M. Alessandrini, A. Palladini, L. De-marchi, and N. Speciale, Expectation Maximization for Joint Deconvolution and Statistics Estimation, Acoust. Imag, vol.30, issue.11, pp.335-343, 2011.
DOI : 10.1007/978-90-481-3255-3_38

N. Zhao, A. Basarab, D. Kouamé, and J. Tourneret, Joint Segmentation and Deconvolution of Ultrasound Images Using a Hierarchical Bayesian Model Based on Generalized Gaussian Priors, IEEE Transactions on Image Processing, vol.25, issue.8, 2015.
DOI : 10.1109/TIP.2016.2567074
URL : https://hal.archives-ouvertes.fr/hal-01374064

M. Pereyra, N. Dobigeon, H. Batatia, and J. Tourneret, Estimating the Granularity Coefficient of a Potts-Markov Random Field Within a Markov Chain Monte Carlo Algorithm, IEEE Transactions on Image Processing, vol.22, issue.6, pp.2385-2397, 2013.
DOI : 10.1109/TIP.2013.2249076

I. Murray, Z. Ghahramani, and D. Mackay, MCMC for doublyintractable distributions, Proc. 22nd Annu. Conf. Uncertainty Artif. Intell (UAI), pp.356-366, 2006.

J. Besag, Spatial interaction and the statistical analysis of lattice systems, J. Roy. Statist. Soc. B (Methodol, vol.36, issue.2, pp.192-236, 1974.

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-00853350

W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109, 1970.
DOI : 10.1093/biomet/57.1.97

R. M. Neal, MCMC using Hamiltonian dynamics, " in Handbook of Markov Chain Monte Carlo (Chapman and Hall/CRC Handbooks of Modern Statistical Methods), 2011.

M. D. Homan and A. Gelman, The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo, J. Mach. Learn. Res, vol.15, issue.1, pp.1593-1623, 2014.

A. Beskos, N. Pillai, G. Roberts, J. Sanz-serna, and A. Stuart, Optimal tuning of the hybrid Monte Carlo algorithm, Bernoulli, vol.19, issue.5A, pp.1501-153412, 2013.
DOI : 10.3150/12-BEJ414

G. Kail, J. Tourneret, F. Hlawatsch, and N. Dobigeon, Blind Deconvolution of Sparse Pulse Sequences Under a Minimum Distance Constraint: A Partially Collapsed Gibbs Sampler Method, IEEE Transactions on Signal Processing, vol.60, issue.6, pp.2727-2743, 2012.
DOI : 10.1109/TSP.2012.2190066

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004.
DOI : 10.1109/TIP.2003.819861

B. M. Asl and A. Mahloojifar, Eigenspace-based minimum variance beamforming applied to medical ultrasound imaging, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.57, issue.11, pp.2381-2390, 2010.
DOI : 10.1109/TUFFC.2010.1706

A. C. Jensen, S. P. Nasholm, C. C. Nilsen, A. Austeng, and S. Holm, Applying Thomson's multitaper approach to reduce speckle in medical ultrasound imaging, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol.59, issue.10, pp.2178-2185, 2012.
DOI : 10.1109/TUFFC.2012.2444

A. Gelman and D. B. Rubin, Inference from Iterative Simulation Using Multiple Sequences, Statistical Science, vol.7, issue.4, pp.457-511, 1992.
DOI : 10.1214/ss/1177011136

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009.
DOI : 10.1137/080716542

J. M. Bioucas-dias and M. A. Figueiredo, A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration, IEEE Transactions on Image Processing, vol.16, issue.12, pp.2992-3004, 2007.
DOI : 10.1109/TIP.2007.909319

J. M. Bioucas-dias, Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors, IEEE Transactions on Image Processing, vol.15, issue.4, pp.937-951, 2006.
DOI : 10.1109/TIP.2005.863972

M. A. Figueiredo, R. D. Nowak, and S. J. Wright, Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems, IEEE Journal of Selected Topics in Signal Processing, vol.1, issue.4, pp.586-597, 2007.
DOI : 10.1109/JSTSP.2007.910281

J. A. Jensen, FIELD: A program for simulating ultrasound systems, Proc. IEEE 10th Nordic-Baltic Conf. Biomed. Imag, pp.351-353, 1996.

P. Flandrin, G. Rilling, and P. Goncalves, Empirical Mode Decomposition as a Filter Bank, IEEE Signal Processing Letters, vol.11, issue.2, pp.112-114, 2004.
DOI : 10.1109/LSP.2003.821662
URL : https://hal.archives-ouvertes.fr/inria-00570615

M. Pereyra, Proximal Markov chain Monte Carlo algorithms, Statistics and Computing, vol.1, issue.4, 2015.
DOI : 10.1007/s11222-015-9567-4

A. Schreck, G. Fort, S. L. Corff, and E. Moulines, A shrinkagethresholding Metropolis adjusted Langevin algorithm for Bayesian variable selection, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00921130

M. Pereyra, 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

J. Gonzalez, Y. Low, A. Gretton, and C. Guestrin, Parallel Gibbs sampling: From colored fields to thin junction trees, Proc. Artif. Intell. Statist. (AISTATS), pp.324-332, 2011.

M. Girolami and B. Calderhead, Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.13, issue.10, pp.123-214, 2011.
DOI : 10.1111/j.1467-9868.2010.00765.x