N. Keshava and J. F. Mustard, Spectral unmixing, IEEE Signal Processing Magazine, vol.19, issue.1, pp.44-57, 2002.
DOI : 10.1109/79.974727

E. Chouzenoux, M. Legendre, S. Moussaoui, and J. Idier, Fast Constrained Least Squares Spectral Unmixing Using Primal-Dual Interior-Point Optimization, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.1, pp.59-69, 2014.
DOI : 10.1109/JSTARS.2013.2266732
URL : https://hal.archives-ouvertes.fr/hal-00828013

Q. Wei, J. Bioucas-dias, N. Dobigeon, J. Tourneret, and I. , Fast spectral unmixing based on Dykstra's alternating projection, Tech. Rep, 2015.

N. Dobigeon, S. Moussaoui, M. Coulon, J. Tourneret, and A. O. Hero, Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery, IEEE Transactions on Signal Processing, vol.57, issue.11, pp.4355-4368, 2009.
DOI : 10.1109/TSP.2009.2025797
URL : https://hal.archives-ouvertes.fr/hal-00455585

C. Chang, X. Zhao, M. L. Althouse, and J. J. Pan, Least squares subspace projection approach to mixed pixel classification for hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, vol.36, issue.3, pp.898-912, 1998.
DOI : 10.1109/36.673681

J. Wang and C. Chang, Applications of Independent Component Analysis in Endmember Extraction and Abundance Quantification for Hyperspectral Imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.44, issue.9, pp.2601-2616, 2006.
DOI : 10.1109/TGRS.2006.874135

D. Manolakis, C. Siracusa, and G. Shaw, Hyperspectral subpixel target detection using the linear mixing model, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.7, pp.1392-1409, 2001.
DOI : 10.1109/36.934072

C. L. Lawson and R. J. Hanson, Solving least squares problems, 1974.
DOI : 10.1137/1.9781611971217

M. Malfait and D. Roose, Wavelet-based image denoising using a Markov random field a priori model, IEEE Transactions on Image Processing, vol.6, issue.4, pp.549-565, 1997.
DOI : 10.1109/83.563320

T. Kasetkasem, M. K. Arora, and P. K. Varshney, Super-resolution land cover mapping using a Markov random field based approach, Remote Sensing of Environment, vol.96, issue.3-4, pp.302-314, 2005.
DOI : 10.1016/j.rse.2005.02.006

C. D. Elia, G. Poggi, and G. Scarpa, A tree-structured Markov random field model for bayesian image segmentation, IEEE Transactions on Image Processing, vol.12, issue.10, pp.1259-1273, 2003.
DOI : 10.1109/TIP.2003.817257

O. Eches, J. A. Benediktsson, N. Dobigeon, and J. Tourneret, Adaptive Markov Random Fields for Joint Unmixing and Segmentation of Hyperspectral Images, IEEE Transactions on Image Processing, vol.22, issue.1, pp.5-16, 2013.
DOI : 10.1109/TIP.2012.2204270

H. Rue and L. Held, Gaussian Markov Random Fields: Theory and Applications, 2005.
DOI : 10.1201/9780203492024

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

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
URL : http://www.cs.unm.edu/~bap/papers/ica-pp-chapter.ps.gz

D. C. Heinz and C. Chang, Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.3, pp.529-545, 2001.
DOI : 10.1109/36.911111

P. Clifford, Markov random fields in statistics Disorder in physical systems: A volume in honour of John M. Hammersley, pp.19-32, 1990.

H. H. Bauschke and P. L. , Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 2011.
DOI : 10.1007/978-3-319-48311-5

R. H. Bartels and G. Stewart, Solution of the matrix equation AX + XB = C [F4], Communications of the ACM, vol.15, issue.9, pp.820-826, 1972.
DOI : 10.1145/361573.361582

N. Komodakis and J. Pesquet, Playing with Duality: An overview of recent primal?dual approaches for solving large-scale optimization problems, IEEE Signal Processing Magazine, vol.32, issue.6, pp.31-54, 2015.
DOI : 10.1109/MSP.2014.2377273
URL : https://hal.archives-ouvertes.fr/hal-01010437

J. M. Bioucas-dias and M. A. Figueiredo, Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pp.1-4, 2010.
DOI : 10.1109/WHISPERS.2010.5594963

]. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Machine Learning, pp.1-122, 2011.
DOI : 10.1561/2200000016

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

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
URL : http://ie.technion.ac.il/%7Ebecka/papers/finalicassp2009.pdf

C. Ricard and F. Debarbieux, Six-color intravital two-photon imaging of brain tumors and their dynamic microenvironment, Frontiers in Cellular Neuroscience, vol.8, pp.8-57, 2014.
DOI : 10.3389/fncel.2014.00057

C. S. Won and H. Derin, Maximum likelihood estimation of Gaussian Markov random field parameters, Proc. IEEE Int. Conf. Acoust., Speech, and Signal Processing (ICASSP), pp.1040-1043, 1988.