L. Chaari, J. Pesquet, A. Benazza-benyahia, and P. Ciuciu, A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging???, Medical Image Analysis, vol.15, issue.2, pp.185-201, 2011.
DOI : 10.1016/j.media.2010.08.001

URL : https://hal.archives-ouvertes.fr/hal-00692260

M. Guerquin-kern, M. Häberlin, K. P. Pruessmann, and M. Unser, A Fast Wavelet-Based Reconstruction Method for Magnetic Resonance Imaging, IEEE Transactions on Medical Imaging, vol.30, issue.9, pp.1649-1660, 2011.
DOI : 10.1109/TMI.2011.2140121

G. Facciolo, A. Almansa, J. Aujol, and V. Caselles, Irregular to Regular Sampling, Denoising, and Deconvolution, Multiscale Modeling & Simulation, vol.7, issue.4, pp.1574-1608, 2009.
DOI : 10.1137/080719443

URL : https://hal.archives-ouvertes.fr/hal-00988783

N. Hajlaoui, C. Chaux, G. Perrin, F. Falzon, and A. Benazza-benyahia, Satellite image restoration in the context of a spatially varying point spread function, Journal of the Optical Society of America A, vol.27, issue.6, pp.1473-1481, 2010.
DOI : 10.1364/JOSAA.27.001473

URL : https://hal.archives-ouvertes.fr/hal-00714260

F. Dupé, M. J. Fadili, and J. Starck, A Proximal Iteration for Deconvolving Poisson Noisy Images Using Sparse Representations, IEEE Transactions on Image Processing, vol.18, issue.2, pp.310-321, 2009.
DOI : 10.1109/TIP.2008.2008223

A. Jezierska, E. Chouzenoux, J. Pesquet, and H. Talbot, A primal-dual proximal splitting approach for restoring data corrupted with poisson-gaussian noise, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), p.4, 2012.
DOI : 10.1109/ICASSP.2012.6288075

URL : https://hal.archives-ouvertes.fr/hal-00687634

B. Vandeghinste, B. Goossens, J. De-beenhouwer, A. Pizurica, W. Philips et al., Split-Bregman-based sparse view CT reconstruction, International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2011.

N. Pustelnik, C. Chaux, J. Pesquet, and C. Comtat, Parallel algorithm and hybrid regularization for dynamic PET reconstruction, IEEE Nuclear Science Symposuim & Medical Imaging Conference, 2010.
DOI : 10.1109/NSSMIC.2010.5874223

URL : https://hal.archives-ouvertes.fr/hal-00733493

J. Anthoine, Y. Aujol, C. Boursier, and . Mélot, Some proximal methods for CBCT and PET tomography, Wavelets and Sparsity XIV, pp.5-09, 2011.
DOI : 10.1117/12.893415

J. Aujol, G. Aubert, L. Blanc-féraud, and A. Chambolle, Image Decomposition into a Bounded Variation Component and an Oscillating Component, Journal of Mathematical Imaging and Vision, vol.15, issue.3, pp.71-88, 2005.
DOI : 10.1007/s10851-005-4783-8

URL : https://hal.archives-ouvertes.fr/hal-00202001

J. Aujol, G. Gilboa, T. Chan, and S. Osher, Structure-Texture Image Decomposition???Modeling, Algorithms, and Parameter Selection, International Journal of Computer Vision, vol.4, issue.2, pp.111-136, 2006.
DOI : 10.1007/s11263-006-4331-z

URL : https://hal.archives-ouvertes.fr/hal-00201977

L. M. Briceño-arias, P. L. Combettes, J. Pesquet, and N. Pustelnik, Proximal Algorithms for Multicomponent Image Recovery Problems, Journal of Mathematical Imaging and Vision, vol.30, issue.1-2, pp.3-22, 2011.
DOI : 10.1007/s10851-010-0243-1

F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, Optimization with Sparsity-Inducing Penalties, Machine Learning, pp.1-106, 2012.
DOI : 10.1561/2200000015

URL : https://hal.archives-ouvertes.fr/hal-00613125

S. Theodoridis, K. Slavakis, and I. Yamada, Adaptive Learning in a World of Projections, IEEE Signal Processing Magazine, vol.28, issue.1, pp.97-123, 2011.
DOI : 10.1109/MSP.2010.938752

C. Chaux, M. El-gheche, J. Farah, J. Pesquet, and B. Pesquet-popescu, A Parallel Proximal Splitting Method for Disparity Estimation from Multicomponent Images Under Illumination Variation, Journal of Mathematical Imaging and Vision, vol.28, issue.4, 2012.
DOI : 10.1007/s10851-012-0361-z

URL : https://hal.archives-ouvertes.fr/hal-00733454

M. Kowalski, E. Vincent, and R. Gribonval, Beyond the Narrowband Approximation: Wideband Convex Methods for Under-Determined Reverberant Audio Source Separation, IEEE Transactions on Audio, Speech, and Language Processing, vol.18, issue.7, pp.1818-1829, 2010.
DOI : 10.1109/TASL.2010.2050089

URL : https://hal.archives-ouvertes.fr/hal-00435897

O. D. Akyildiz and I. Bayram, An analysis prior based decomposition method for audio signals, Proc. Eur. Sig. and Image Proc. Conference, 2012.

P. L. Combettes and J. Pesquet, Proximal splitting methods in signal processing, " in Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp.185-212, 2011.

S. Setzer, G. Steidl, and T. Teuber, Deblurring Poissonian images by split Bregman techniques, Journal of Visual Communication and Image Representation, vol.21, issue.3, pp.193-199, 2010.
DOI : 10.1016/j.jvcir.2009.10.006

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.564.8327

M. A. Figueiredo and J. M. Bioucas-dias, Restoration of Poissonian Images Using Alternating Direction Optimization, IEEE Transactions on Image Processing, vol.19, issue.12, pp.3133-3145, 2010.
DOI : 10.1109/TIP.2010.2053941

J. Pesquet and N. Pustelnik, A parallel inertial proximal optimization method, Pac. J. Optim, vol.8, issue.2, pp.273-305, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00790702

P. L. Combettes and V. R. Wajs, Signal Recovery by Proximal Forward-Backward Splitting, Multiscale Modeling & Simulation, vol.4, issue.4, pp.1168-1200, 2005.
DOI : 10.1137/050626090

URL : https://hal.archives-ouvertes.fr/hal-00017649

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

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

P. L. Combettes and J. Pesquet, A proximal decomposition method for solving convex variational inverse problems, Inverse Problems, vol.24, issue.6, 2008.
DOI : 10.1088/0266-5611/24/6/065014

URL : https://hal.archives-ouvertes.fr/hal-00692901

N. P. Galatsanos and A. K. Katsaggelos, Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation, IEEE Transactions on Image Processing, vol.1, issue.3, pp.322-336, 1992.
DOI : 10.1109/83.148606

P. C. Hansen and D. P. Leary, The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems, SIAM Journal on Scientific Computing, vol.14, issue.6, pp.1487-1503, 1993.
DOI : 10.1137/0914086

A. Pizurica and W. Philips, Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising, IEEE Transactions on Image Processing, vol.15, issue.3, pp.654-665, 2006.
DOI : 10.1109/TIP.2005.863698

S. Ramani, T. Blu, and M. Unser, Monte-Carlo Sure: A Black-Box Optimization of Regularization Parameters for General Denoising Algorithms, IEEE Transactions on Image Processing, vol.17, issue.9, pp.1540-1554, 2008.
DOI : 10.1109/TIP.2008.2001404

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

D. C. Youla and H. Webb, Image Restoration by the Method of Convex Projections: Part 1ߞTheory, IEEE Transactions on Medical Imaging, vol.1, issue.2, pp.81-94, 1982.
DOI : 10.1109/TMI.1982.4307555

H. J. Trussell and M. R. Civanlar, The feasible solution in signal restoration, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.32, issue.2, pp.201-212, 1984.
DOI : 10.1109/TASSP.1984.1164297

P. L. Combettes, Inconsistent signal feasibility problems: least-squares solutions in a product space, IEEE Transactions on Signal Processing, vol.42, issue.11, pp.2955-2966, 1994.
DOI : 10.1109/78.330356

K. Kose, V. Cevher, and A. E. Cetin, Filtered Variation method for denoising and sparse signal processing, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012.
DOI : 10.1109/ICASSP.2012.6288628

T. Teuber, G. Steidl, and R. H. Chan, -divergence constraints, Inverse Problems, vol.29, issue.3, 2012.
DOI : 10.1088/0266-5611/29/3/035007

URL : https://hal.archives-ouvertes.fr/hal-00929638

R. Ciak, B. Shafei, and G. Steidl, Homogeneous Penalizers and Constraints in Convex Image Restoration, Journal of Mathematical Imaging and Vision, vol.21, issue.4, 2012.
DOI : 10.1007/s10851-012-0392-5

L. Jacques, L. Duval, C. Chaux, and G. Peyré, A panorama on multiscale geometric representations, intertwining spatial, directional and frequency selectivity, Signal Processing, vol.91, issue.12, pp.2699-2730, 2011.
DOI : 10.1016/j.sigpro.2011.04.025

URL : https://hal.archives-ouvertes.fr/hal-01330604

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

M. V. Afonso, J. M. Bioucas-dias, and M. A. Figueiredo, An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems, IEEE Transactions on Image Processing, vol.20, issue.3, pp.681-695, 2011.
DOI : 10.1109/TIP.2010.2076294

A. Tikhonov, Tikhonov regularization of incorrectly posed problems, Soviet Mathematics Doklady, vol.4, pp.1624-1627, 1963.

E. Chouzenoux, J. Idier, and S. Moussaoui, A Majorize–Minimize Strategy for Subspace Optimization Applied to Image Restoration, IEEE Transactions on Image Processing, vol.20, issue.6, pp.1517-1528, 2011.
DOI : 10.1109/TIP.2010.2103083

G. Aubert and R. Tahraoui, Sur la minimisation d'une fonctionnelle non convexe, non différentiable en dimension 1, Bolletino UMI, vol.5, issue.17B, 1980.

A. Ben-tal and M. Teboulle, A smoothing technique for nondifferentiable optimization problems, Lecture Notes in Mathematics, vol.1405, pp.1-11, 1989.
DOI : 10.1007/BFb0083582

J. Hiriart-urruty and C. Lemaréchal, Convex analysis and minimization algorithms, Part I : Fundamentals, 1996.
DOI : 10.1007/978-3-662-02796-7

P. Tseng, Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization, Journal of Optimization Theory and Applications, vol.109, issue.3, pp.475-494, 2001.
DOI : 10.1023/A:1017501703105

S. J. Wright, Primal-dual interior-point methods, 1997.
DOI : 10.1137/1.9781611971453

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.5633

L. M. Bregman, The method of successive projection for finding a common point of convex sets, Soviet Math. Dokl, vol.6, pp.688-692, 1965.

L. G. Gurin, B. T. Polyak, and E. V. Raik, Projection methods for finding a common point of convex sets, Zh. Vychisl. Mat. Mat. Fiz, vol.7, issue.6, pp.1211-1228, 1967.

P. L. Combettes, The foundations of set theoretic estimation, Proceedings of the IEEE, vol.81, issue.2, pp.182-208, 1993.
DOI : 10.1109/5.214546

P. L. Combettes, Convex set theoretic image recovery by extrapolated iterations of parallel subgradient projections, IEEE Transactions on Image Processing, vol.6, issue.4, pp.492-506, 1997.
DOI : 10.1109/83.563316

Y. Censor, W. Chen, P. L. Combettes, R. Davidi, and G. T. Herman, On the effectiveness of projection methods for convex feasibility problems with linear inequality constraints, Computational Optimization and Applications, vol.44, issue.3, pp.1065-1088, 2012.
DOI : 10.1007/s10589-011-9401-7

URL : https://hal.archives-ouvertes.fr/hal-00643783

B. T. Polyak, Minimization of unsmooth functionals, USSR Computational Mathematics and Mathematical Physics, vol.9, issue.3, pp.14-29, 1969.
DOI : 10.1016/0041-5553(69)90061-5

N. Z. Shor, Minimization Methods for Non-differentiable Functions, 1985.
DOI : 10.1007/978-3-642-82118-9

P. L. Combettes, A block-iterative surrogate constraint splitting method for quadratic signal recovery, IEEE Transactions on Signal Processing, vol.51, issue.7, pp.1771-1782, 2003.
DOI : 10.1109/TSP.2003.812846

K. Slavakis, I. Yamada, and N. Ogura, The Adaptive Projected Subgradient Method over the Fixed Point Set of Strongly Attracting Nonexpansive Mappings, Numerical Functional Analysis and Optimization, vol.87, issue.7-8, pp.905-930, 2006.
DOI : 10.1109/78.995065

P. Bouboulis, K. Slavakis, and S. Theodoridis, Adaptive Learning in Complex Reproducing Kernel Hilbert Spaces Employing Wirtinger's Subgradients, IEEE Transactions on Neural Networks and Learning Systems, vol.23, issue.3, pp.425-438, 2012.
DOI : 10.1109/TNNLS.2011.2179810

J. J. Moreau, Proximit?? et dualit?? dans un espace hilbertien, Bulletin de la Société mathématique de France, vol.79, pp.273-299, 1965.
DOI : 10.24033/bsmf.1625

URL : http://archive.numdam.org/article/BSMF_1965__93__273_0.pdf

D. L. Donoho, De-noising by soft-thresholding, IEEE Transactions on Information Theory, vol.41, issue.3, pp.613-627, 1995.
DOI : 10.1109/18.382009

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

M. Figueiredo, R. Nowak, and S. 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.586-598, 2007.
DOI : 10.1109/JSTSP.2007.910281

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

M. Fornasier and C. Schönlieb, Subspace Correction Methods for Total Variation and $\ell_1$-Minimization, SIAM Journal on Numerical Analysis, vol.47, issue.5, pp.3397-3428, 2009.
DOI : 10.1137/070710779

G. Steidl and T. Teuber, Removing Multiplicative Noise by Douglas-Rachford Splitting Methods, Journal of Mathematical Imaging and Vision, vol.11, issue.11, pp.168-184, 2010.
DOI : 10.1007/s10851-009-0179-5

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.566.9163

G. Chen and M. Teboulle, A proximal-based decomposition method for convex minimization problems, Mathematical Programming, vol.29, issue.1-3, pp.81-101, 1994.
DOI : 10.1007/BF01582566

E. Esser, X. Zhang, and T. Chan, A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science, SIAM Journal on Imaging Sciences, vol.3, issue.4, pp.1015-1046, 2010.
DOI : 10.1137/09076934X

A. Chambolle and T. Pock, A First-Order Primal-Dual Algorithm for Convex Problems with??Applications to Imaging, Journal of Mathematical Imaging and Vision, vol.60, issue.5, pp.120-145, 2011.
DOI : 10.1007/s10851-010-0251-1

URL : https://hal.archives-ouvertes.fr/hal-00490826

L. M. Briceño-arias and P. L. Combettes, A Monotone+Skew Splitting Model for Composite Monotone Inclusions in Duality, SIAM Journal on Optimization, vol.21, issue.4, pp.1230-1250, 2011.
DOI : 10.1137/10081602X

P. L. Combettes and J. Pesquet, Primal-Dual Splitting Algorithm for Solving Inclusions with Mixtures of Composite, Lipschitzian, and Parallel-Sum Type Monotone Operators, Set-Valued and Variational Analysis, vol.38, issue.2, 2011.
DOI : 10.1007/s11228-011-0191-y

URL : https://hal.archives-ouvertes.fr/hal-00794044

B. C. V?uv?u, A splitting algorithm for dual monotone inclusions involving cocoercive operators, Adv. Comput. Math, 2011.

L. Condat, A Primal???Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms, Journal of Optimization Theory and Applications, vol.23, issue.1???2, 2012.
DOI : 10.1007/s10957-012-0245-9

URL : https://hal.archives-ouvertes.fr/hal-00609728

N. Pustelnik, J. Pesquet, and C. Chaux, Relaxing Tight Frame Condition in Parallel Proximal Methods for Signal Restoration, IEEE Transactions on Signal Processing, vol.60, issue.2, pp.968-973, 2012.
DOI : 10.1109/TSP.2011.2173684

URL : https://hal.archives-ouvertes.fr/hal-00692256

C. Couprie, L. Grady, L. Najman, J. Pesquet, and H. Talbot, Dual Constrained TV-based Regularization on Graphs, SIAM Journal on Imaging Sciences, vol.6, issue.3, 2012.
DOI : 10.1137/120895068

URL : https://hal.archives-ouvertes.fr/hal-00743968

H. H. Bauschke and P. L. , Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 2011.

J. Wu, F. Liu, L. C. Jiao, X. Wang, and B. Hou, Multivariate Compressive Sensing for Image Reconstruction in the Wavelet Domain: Using Scale Mixture Models, IEEE Transactions on Image Processing, vol.20, issue.12, pp.3483-3494, 2011.
DOI : 10.1109/TIP.2011.2150231

L. 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

J. Aujol, Some First-Order Algorithms for Total Variation Based Image Restoration, Journal of Mathematical Imaging and Vision, vol.33, issue.2, pp.307-327, 2009.
DOI : 10.1007/s10851-009-0149-y

URL : https://hal.archives-ouvertes.fr/hal-00260494

B. A. Turlach, W. N. Venables, and S. J. Wright, Simultaneous Variable Selection, Technometrics, vol.47, issue.3, pp.349-363, 2005.
DOI : 10.1198/004017005000000139

A. Quattoni, X. Carreras, M. Collins, and T. Darrell, An efficient projection for ? 1,? regularization, International Conference on Machine Learning, 2009.

Y. Chen and A. O. Hero, Recursive <formula formulatype="inline"><tex Notation="TeX">$\ell_{1,\infty}$</tex> </formula> Group Lasso, IEEE Transactions on Signal Processing, vol.60, issue.8, pp.3978-3987, 2012.
DOI : 10.1109/TSP.2012.2192924

URL : http://arxiv.org/abs/1101.5734

E. J. Candés, M. B. Wakin, and S. Boyd, Enhancing Sparsity by Reweighted ??? 1 Minimization, Journal of Fourier Analysis and Applications, vol.7, issue.3, pp.877-905, 2008.
DOI : 10.1007/s00041-008-9045-x

G. Gilboa and S. Osher, Nonlocal Operators with Applications to Image Processing, Multiscale Modeling & Simulation, vol.7, issue.3, p.1005, 2009.
DOI : 10.1137/070698592

G. Peyré and J. Fadili, Group sparsity with overlapping partition functions, Proc. Eur. Sig. and Image Proc. Conference, p.5, 2011.

I. Bayram and M. Kamasak, Directional Total Variation, IEEE Signal Processing Letters, vol.19, issue.12, 2012.
DOI : 10.1109/LSP.2012.2220349

URL : http://dx.doi.org/10.5281/zenodo.42773

E. Van-den, M. P. Berg, and . Friedlander, Probing the Pareto Frontier for Basis Pursuit Solutions, SIAM Journal on Scientific Computing, vol.31, issue.2, pp.890-912, 2008.
DOI : 10.1137/080714488

P. Weiss, L. Blanc-féraud, and G. Aubert, Efficient Schemes for Total Variation Minimization Under Constraints in Image Processing, SIAM Journal on Scientific Computing, vol.31, issue.3, pp.2047-2080, 2009.
DOI : 10.1137/070696143

URL : https://hal.archives-ouvertes.fr/inria-00166096

J. M. Fadili and G. Peyré, Total Variation Projection With First Order Schemes, IEEE Transactions on Image Processing, vol.20, issue.3, pp.657-669, 2011.
DOI : 10.1109/TIP.2010.2072512

URL : https://hal.archives-ouvertes.fr/hal-00401251

X. Zhang, M. Burger, X. Bresson, and S. Osher, Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction, SIAM Journal on Imaging Sciences, vol.3, issue.3, pp.253-276, 2010.
DOI : 10.1137/090746379

A. Buades, B. Coll, and J. Morel, A Review of Image Denoising Algorithms, with a New One, Multiscale Modeling & Simulation, vol.4, issue.2, pp.490-530, 2005.
DOI : 10.1137/040616024

URL : https://hal.archives-ouvertes.fr/hal-00271141

A. Foi and G. Boracchi, Foveated self-similarity in nonlocal image filtering, Human Vision and Electronic Imaging XVII, 2012.
DOI : 10.1117/12.912217

G. Gilboa and S. Osher, Nonlocal Linear Image Regularization and Supervised Segmentation, Multiscale Modeling & Simulation, vol.6, issue.2, pp.595-630, 2007.
DOI : 10.1137/060669358

Z. Wang and A. C. Bovik, Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures, IEEE Signal Processing Magazine, vol.26, issue.1, pp.98-117, 2009.
DOI : 10.1109/MSP.2008.930649

M. Lebrun, M. Colom, A. Buades, and J. M. , Secrets of image denoising cuisine, Acta Numerica, vol.21, pp.475-576, 2012.
DOI : 10.1017/S0962492912000062

C. Kervrann and J. Boulanger, Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation, International Journal of Computer Vision, vol.27, issue.2, pp.45-69, 2008.
DOI : 10.1007/s11263-007-0096-2

R. Tibshirani, Regression shrinkage and selection via the lasso, J. R. Statist. Soc. B, vol.58, pp.267-288, 1996.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.49-67, 2006.
DOI : 10.1198/016214502753479356

R. Gaetano, G. Chierchia, and B. Pesquet-popescu, Parallel implementations of a disparity estimation algorithm based on a Proximal splitting method, 2012 Visual Communications and Image Processing, 2012.
DOI : 10.1109/VCIP.2012.6410734

I. Ekeland and R. Témam, Convex analysis and variational problems, 1999.
DOI : 10.1137/1.9781611971088

P. L. Combettes and J. Pesquet, Proximal Thresholding Algorithm for Minimization over Orthonormal Bases, SIAM Journal on Optimization, vol.18, issue.4, pp.1351-1376, 2007.
DOI : 10.1137/060669498

URL : https://hal.archives-ouvertes.fr/hal-00621819