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

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

J. Weickert, B. T. Romeny, and M. Viergever, Efficient and reliable schemes for nonlinear diffusion filtering, IEEE Transactions on Image Processing, vol.7, issue.3, pp.398-410, 1998.
DOI : 10.1109/83.661190

R. Devore and B. Lucier, Fast wavelet techniques for near-optimal image processing, MILCOM 92 Conference Record, pp.48-51, 1992.
DOI : 10.1109/MILCOM.1992.244110

M. Aharon, M. Elad, and A. Bruckstein, The K-SVD algorithm, Proceedings of SPARSE'05, 2005.

M. Lustig, D. Donoho, and J. M. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging, Magnetic Resonance in Medicine, vol.170, issue.6, pp.1182-1195, 2007.
DOI : 10.1002/mrm.21391

E. Candès, J. Romberg, and T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, vol.52, issue.2, pp.489-509, 2006.
DOI : 10.1109/TIT.2005.862083

E. Candès and J. Romberg, Practical signal recovery from random projections, Proceedings of the SPIE Conference on Wavelet Applications in Signal and Image Processing XI, p.5914, 2005.

D. L. Donoho, Compressed sensing, IEEE Transactions on Information Theory, vol.52, issue.4, pp.1289-1306, 2006.
DOI : 10.1109/TIT.2006.871582
URL : https://hal.archives-ouvertes.fr/inria-00369486

D. Donoho, M. Elad, and V. Temlyakov, Stable recovery of sparse overcomplete representations in the presence of noise, IEEE Transactions on Information Theory, vol.52, issue.1, pp.6-18, 2006.
DOI : 10.1109/TIT.2005.860430

S. S. Chen, D. L. Donoho, and M. A. Saunders, Atomic Decomposition by Basis Pursuit, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.33-61, 1998.
DOI : 10.1137/S1064827596304010
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.7694

E. Candès and J. Romberg, Sparsity and incoherence in compressive sampling, Inverse Problems, vol.23, issue.3, pp.969-985, 2006.
DOI : 10.1088/0266-5611/23/3/008

A. Cohen, R. Devore, P. Petrushev, and H. Xu, Nonlinear approximation and the space BV(R 2 ) 1, American Journal of Mathematics, vol.121, pp.587-628, 1999.

T. F. Chan, S. Osher, and J. Shen, The digital TV filter and nonlinear denoising, IEEE Transactions on Image Processing, vol.10, issue.2, pp.231-241, 2001.
DOI : 10.1109/83.902288
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.3483

E. Candès, J. Romberg, and T. Tao, Stable signal recovery from incomplete and inaccurate measurements, Communications on Pure and Applied Mathematics, vol.7, issue.8, pp.1207-1223, 2006.
DOI : 10.1002/cpa.20124

T. Lindeberg, Scale-Space Theory in Computer Vision. The Kluwer International Series in Engineering and Computer Science, 1994.
DOI : 10.1007/978-1-4757-6465-9
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.3577

G. Gilboa, N. Sochen, and Y. Y. Zeevi, Variational denoising of partly textured images by spatially varying constraints, IEEE Transactions on Image Processing, vol.15, issue.8, pp.2281-2289, 2006.
DOI : 10.1109/TIP.2006.875247