J. Pesquet, A. Benazza-benyahia, and C. Chaux, A SURE Approach for Digital Signal/Image Deconvolution Problems, IEEE Transactions on Signal Processing, vol.57, issue.12, pp.4616-4632, 2009.
DOI : 10.1109/TSP.2009.2026077
URL : https://hal.archives-ouvertes.fr/hal-00621942

A. K. Nandi, D. Mampel, and B. Roscher, Blind deconvolution of ultrasonic signals in nondestructive testing applications, IEEE Transactions on Signal Processing, vol.45, issue.5, pp.1382-1390, 1997.
DOI : 10.1109/78.575716

K. F. Kaaresen and T. Taxt, Multichannel blind deconvolution of seismic signals, GEOPHYSICS, vol.63, issue.6, pp.2093-2107, 1998.
DOI : 10.1190/1.1444503

A. K. Takahata, E. Z. Nadalin, R. Ferrari, L. T. Duarte, R. Suyama et al., Unsupervised Processing of Geophysical Signals: A Review of Some Key Aspects of Blind Deconvolution and Blind Source Separation, IEEE Signal Processing Magazine, vol.29, issue.4, pp.27-35, 2012.
DOI : 10.1109/MSP.2012.2189999

M. Q. Pham, L. Duval, C. Chaux, and J. Pesquet, A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal, IEEE Transactions on Signal Processing, vol.62, issue.16, pp.4256-4269, 2014.
DOI : 10.1109/TSP.2014.2331614
URL : https://hal.archives-ouvertes.fr/hal-00914628

D. Kundur and D. Hatzinakos, Blind image deconvolution, IEEE Signal Processing Magazine, vol.13, issue.3, pp.43-64, 1996.
DOI : 10.1109/79.489268

D. Kundur and D. Hatzinakos, Blind image deconvolution revisited, IEEE Signal Processing Magazine, vol.13, issue.6, pp.61-63, 1996.
DOI : 10.1109/79.543976

M. Kato, I. Yamada, and K. Sakaniwa, A set-theoretic blind image deconvolution based on hybrid steepest descent method, IEICE Trans. Fund. Electron. Comm. Comput. Sci, issue.8, pp.1443-1449, 1999.

A. Ahmed, B. Recht, and J. Romberg, Blind Deconvolution Using Convex Programming, IEEE Transactions on Information Theory, vol.60, issue.3, pp.1711-1732, 2014.
DOI : 10.1109/TIT.2013.2294644

P. Comon, ´. E. Moreau, and J. Pesquet, Contrasts for multichannel blind deconvolution Generalized contrasts for multichannel blind deconvolution of linear systems, Signal Process. Lett. Signal Process. Lett, vol.313, issue.4 6, pp.209-211, 1996.

M. Zibulevsky and B. A. Pearlmutter, Blind Source Separation by Sparse Decomposition in a Signal Dictionary, Neural Computation, vol.1, issue.4, pp.863-882, 2001.
DOI : 10.1016/S0042-6989(97)00169-7

P. Hoyer, Non-negative matrix factorization with sparseness constraints, J. Mach. Learn. Res, vol.5, pp.1457-1469, 2004.

N. Hurley and S. Rickard, Comparing Measures of Sparsity, IEEE Transactions on Information Theory, vol.55, issue.10, pp.4723-4741, 2009.
DOI : 10.1109/TIT.2009.2027527

B. Barak, J. Kelner, and D. Steurer, Rounding sum-of-squares relaxations, Proceedings of the 46th Annual ACM Symposium on Theory of Computing, STOC '14, 2014.
DOI : 10.1145/2591796.2591886

M. Mørup, K. H. Madsen, and L. K. Hansen, Approximate L0 constrained non-negative matrix and tensor factorization, Proc. Int. Symp. Circuits Syst, pp.1328-1331, 2008.

W. C. Gray, Variable norm deconvolution, 1978.

H. Ji, J. Li, Z. Shen, and K. Wang, Image deconvolution using a characterization of sharp images in wavelet domain, Applied and Computational Harmonic Analysis, vol.32, issue.2, pp.295-304, 2012.
DOI : 10.1016/j.acha.2011.09.006

L. Demanet and P. Hand, Scaling law for recovering the sparsest element in a subspace, Information and Inference, vol.3, issue.4, 2014.
DOI : 10.1093/imaiai/iau007

A. Benichoux, E. Vincent, and R. Gribonval, A fundamental pitfall in blind deconvolution with sparse and shift-invariant priors, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013.
DOI : 10.1109/ICASSP.2013.6638838
URL : https://hal.archives-ouvertes.fr/hal-00800770

D. Krishnan, T. Tay, and R. Fergus, Blind deconvolution using a normalized sparsity measure, CVPR 2011
DOI : 10.1109/CVPR.2011.5995521

E. Esser, Y. Lou, and J. Xin, A Method for Finding Structured Sparse Solutions to Nonnegative Least Squares Problems with Applications, SIAM Journal on Imaging Sciences, vol.6, issue.4, pp.2010-2046, 2013.
DOI : 10.1137/13090540X

J. Bolte, S. Sabach, and M. Teboulle, Proximal alternating linearized minimization fon nonconvex and nonsmooth problems, Math. Progr. (Ser. A), 2013.

E. Chouzenoux, J. Pesquet, and A. Repetti, A block coordinate variable metric forward???backward algorithm, Journal of Global Optimization, vol.6, issue.3, 2013.
DOI : 10.1007/s10898-016-0405-9
URL : https://hal.archives-ouvertes.fr/hal-00945918

S. Sotthivirat and J. A. Fessler, Image recovery using partitioned-separable paraboloidal surrogate coordinate ascent algorithms, IEEE Transactions on Image Processing, vol.11, issue.3, pp.306-317, 2002.
DOI : 10.1109/83.988963

E. Chouzenoux, J. Pesquet, and A. Repetti, Variable Metric Forward???Backward Algorithm for Minimizing the Sum of a Differentiable Function and a Convex Function, Journal of Optimization Theory and Applications, vol.21, issue.2, pp.107-132, 2014.
DOI : 10.1007/s10957-013-0465-7
URL : https://hal.archives-ouvertes.fr/hal-00789970

P. L. Combettes and B. C. V?uv?u, Variable metric quasi-Fej??r monotonicity, Nonlinear Analysis: Theory, Methods & Applications, vol.78, pp.17-31, 2013.
DOI : 10.1016/j.na.2012.09.008

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

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

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

P. L. Combettes, J. Pesquet, H. H. Bauschke, R. Burachik, and P. , Proximal splitting methods in signal processing, " in Fixed-point algorithms for inverse problems in science and engineering, pp.185-212, 2011.

K. Slavakis, Y. Kopsinis, S. Theodoridis, and S. Mclaughlin, Generalized Thresholding and Online Sparsity-Aware Learning in a Union of Subspaces, IEEE Transactions on Signal Processing, vol.61, issue.15, pp.3760-3773, 2013.
DOI : 10.1109/TSP.2013.2264464

Z. Q. Luo and P. Tseng, On the convergence of the coordinate descent method for convex differentiable minimization, Journal of Optimization Theory and Applications, vol.34, issue.B, pp.7-35, 1992.
DOI : 10.1007/BF00939948

J. Bolte, P. L. Combettes, and J. Pesquet, Alternating proximal algorithm for blind image recovery, 2010 IEEE International Conference on Image Processing, pp.1673-1676, 2010.
DOI : 10.1109/ICIP.2010.5652173
URL : https://hal.archives-ouvertes.fr/hal-00844115

Y. Xu and W. Yin, A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion, SIAM Journal on Imaging Sciences, vol.6, issue.3, pp.1758-1789, 2013.
DOI : 10.1137/120887795

M. Allain, J. Idier, and Y. Goussard, On global and local convergence of half-quadratic algorithms, IEEE Transactions on Image Processing, vol.15, issue.5, pp.1130-1142, 2006.
DOI : 10.1109/TIP.2005.864173
URL : https://hal.archives-ouvertes.fr/hal-00400663

T. Ulrych and M. D. Sacchi, Information-based inversion and processing with applications, 2005.

A. T. Walden and J. W. Hosken, THE NATURE OF THE NON-GAUSSIANITY OF PRIMARY REFLECTION COEFFICIENTS AND ITS SIGNIFICANCE FOR DECONVOLUTION*, Geophysical Prospecting, vol.55, issue.7, pp.1038-1066, 1986.
DOI : 10.1016/0016-7142(78)90005-4

N. Ricker, THE FORM AND NATURE OF SEISMIC WAVES AND THE STRUCTURE OF SEISMOGRAMS, GEOPHYSICS, vol.5, issue.4, pp.348-366, 1940.
DOI : 10.1190/1.1441816

P. Loganathan, A. W. Khong, and P. A. Naylor, A Class of Sparseness-Controlled Algorithms for Echo Cancellation, IEEE Transactions on Audio, Speech, and Language Processing, vol.17, issue.8, pp.1591-1601, 2009.
DOI : 10.1109/TASL.2009.2025903

T. Drugman, Maximum Phase Modeling for Sparse Linear Prediction of Speech, IEEE Signal Processing Letters, vol.21, issue.2, pp.185-189, 2014.
DOI : 10.1109/LSP.2013.2296944

M. Yukawa, Y. Tawara, S. Sasaki, and I. Yamada, A sparsity-based design of regularization parameter for adaptive proximal forward-backward splitting algorithm, Proc. Int. Symp. Wireless Comm. Syst, pp.1-4, 2013.

X. Chang, Y. Wang, R. Li, and Z. Xu, Sparse K-means with ??/?0 penalty for high-dimensional data clustering, 2014.