V. Kolmogorov and R. Zabih, Computing visual correspondence with occlusions using graph cuts, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.508-515, 2001.
DOI : 10.1109/ICCV.2001.937668

URL : http://ce.sharif.edu/~elno/disparitymap/Papers/KZ-ICCV01.pdf

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2011.
DOI : 10.1109/34.969114

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

A. Klaus, M. Sormann, and K. Karner, Segment-based stereo matching using belief propagation and a self-adapting

I. J. Cox, S. Roy, and S. L. Hingorani, Dynamic histogram warping of image pairs for constant image brightness, Proceedings., International Conference on Image Processing, pp.366-369, 1995.
DOI : 10.1109/ICIP.1995.537491

R. Zabih and J. Woodfill, Non-parametric local transforms for computing visual correspondence, Proc. Eur. Conf. Comput . Vis, pp.15-158, 1994.
DOI : 10.1007/BFb0028345

J. E. Davis, R. Yang, and L. Wang, BRDF invariant stereo using light transport constancy, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
DOI : 10.1109/ICCV.2005.51

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

M. A. Gennert, Brightness-based Stereo Matching, [1988 Proceedings] Second International Conference on Computer Vision, pp.139-143, 1988.
DOI : 10.1109/CCV.1988.589984

J. Pesquet and N. Pustelnik, A parallel inertial proximal optimization method, Pac. J. Op- tim, issue.11, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00790702

A. Fusiello, E. Trucco, and A. Verri, A compact algorithm for rectification of stereo pairs, Machine Vision and Applications, vol.12, issue.1, pp.16-22, 2000.
DOI : 10.1007/s001380050120

D. Tsai, C. Lin, and J. Chen, The evaluation of normalized cross correlations for defect detection, Pattern Recognition Letters, vol.24, issue.15, pp.2525-2535, 2003.
DOI : 10.1016/S0167-8655(03)00098-9

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

P. L. Combettes and J. Pesquet, Image Restoration Subject to a Total Variation Constraint, IEEE Transactions on Image Processing, vol.13, issue.9, pp.1213-1222, 2004.
DOI : 10.1109/TIP.2004.832922

URL : https://hal.archives-ouvertes.fr/hal-00017934

P. L. Combettes and J. Pesquet, A proximal decomposition method for solving convex variational inverse problems, Inverse Problems, vol.24, issue.6, p.27, 2008.
DOI : 10.1088/0266-5611/24/6/065014

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

N. Pustelnik, C. Chaux, and J. Pesquet, Parallel Proximal Algorithm for Image Restoration Using Hybrid Regularization, IEEE Transactions on Image Processing, vol.20, issue.9, pp.2450-2462, 2011.
DOI : 10.1109/TIP.2011.2128335

URL : https://hal.archives-ouvertes.fr/hal-00826121

J. F. 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

A. Chambolle, R. A. Devore, N. Lee, and B. J. Lucier, Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage, IEEE Transactions on Image Processing, vol.7, issue.3, pp.319-335, 1998.
DOI : 10.1109/83.661182

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

D. Han and D. R. Larson, Frames, bases and group representations, Mem. Amer, p.94, 2000.
DOI : 10.1090/memo/0697

S. Mallat, A Wavelet Tour of Signal Processing, 1997.

S. Lefkimmiatis, A. Bourquard, and M. Unser, Hessian-Based Norm Regularization for Image Restoration With Biomedical Applications, IEEE Transactions on Image Processing, vol.21, issue.3, pp.983-995, 2012.
DOI : 10.1109/TIP.2011.2168232

J. J. Moreau, Fonctions convexes duales et points proximaux dans un espace hilbertien, C. R. Acad. Sci, vol.255, pp.2897-2899, 1962.

P. L. Combettes, The foundations of set theoretic estimation, Proc. IEEE, pp.182-208, 1993.

P. L. Combettes, J. Pesquet, H. H. Bauschke, R. Burachik, P. L. Combettes et al., Proximal Splitting Methods in Signal Processing, Fixed-Point Algorithms for Inverse Problems in Science and Engineering, pp.185-212, 2011.
DOI : 10.1007/978-1-4419-9569-8_10

URL : https://hal.archives-ouvertes.fr/hal-00643807

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

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

Y. C. Eldar and M. Mishali, Robust Recovery of Signals From a Structured Union of Subspaces, IEEE Transactions on Information Theory, vol.55, issue.11, pp.5302-5316, 2009.
DOI : 10.1109/TIT.2009.2030471

M. Afonso, J. 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

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=

H. Attouch and M. Soueycatt, Augmented Lagrangian and proximal alternating direction methods of multipliers in Hilbert spaces -applications to games, PDE's and control, Pac. J. Optim, vol.5, issue.1, pp.17-37, 2009.

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

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

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

P. L. Combettes, Ð. D?ungd?ung, and B. C. V?uv?u, Proximity for sums of composite functions, Journal of Mathematical Analysis and Applications, vol.380, issue.2, pp.680-688, 2011.
DOI : 10.1016/j.jmaa.2011.02.079

URL : https://hal.archives-ouvertes.fr/hal-00643804

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
DOI : 10.1007/s11228-011-0191-y

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

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

K. Yoon and I. Kweon, Adaptive support-weight approach for correspondence search, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.4, pp.650-656, 2006.
DOI : 10.1109/TPAMI.2006.70

A. L. Yuille and T. Poggio, A generalized ordering constraint for stereo correspondence, 1984.

T. Pock, T. Schoenemann, G. Graber, H. Bischof, and D. Cremers, A convex formulation of continuous multilabel problems, Proc. Eur. Conf. Comput. Vis, pp.792-805, 2008.

L. Wang and R. Yang, Global stereo matching leveraged by sparse ground control points, CVPR 2011, pp.3033-3040, 2011.
DOI : 10.1109/CVPR.2011.5995480

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

D. Min, J. Lu, and M. Do, A revisit to cost aggregation in stereo matching: How far can we reduce its computational redundancy?, 2011 International Conference on Computer Vision, pp.1567-1574, 2011.
DOI : 10.1109/ICCV.2011.6126416

D. Mukherjee, G. Wang, and J. Wu, Stereo matching algorithm based on curvelet decomposition and modified support weights, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.758-761, 2010.
DOI : 10.1109/ICASSP.2010.5495003