C. Aguerrebere, A. Almansa, J. Delon, Y. Gousseau, and P. Musé, A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, With an Application to HDR Imaging, IEEE Transactions on Computational Imaging, vol.3, issue.4, 2017.
DOI : 10.1109/TCI.2017.2704439
URL : https://hal.archives-ouvertes.fr/hal-01107519

C. Aguerrebere, J. Delon, Y. Gousseau, and P. Musé, Study of the digital camera acquisition process and statistical modeling of the sensor raw data, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00733538

S. Awate and R. Whitaker, Image denoising with unsupervised information-theoretic adaptive filtering, International Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp.44-51, 2004.
DOI : 10.1109/tpami.2006.64
URL : http://www.cs.utah.edu/projects/sci/publications/awate05/Awate_JPAMI05.pdf

M. Christopher and . Bishop, Pattern recognition and machine learning, 2006.

A. Buades, B. Coll, and J. M. , A Review of Image Denoising Algorithms, with a New One, Multiscale Modeling & Simulation, vol.4, issue.2, pp.490-530, 2006.
DOI : 10.1137/040616024
URL : https://hal.archives-ouvertes.fr/hal-00271141

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering, IEEE Transactions on Image Processing, vol.16, issue.8, pp.2080-2095, 2007.
DOI : 10.1109/TIP.2007.901238
URL : http://sp.cs.tut.fi/publications/archive/Dabov2007-Image.pdf

C. Deledalle, L. Denis, and F. Tupin, How to Compare Noisy Patches? Patch Similarity Beyond Gaussian Noise, International Journal of Computer Vision, vol.21, issue.11, pp.86-102, 2012.
DOI : 10.1016/S0262-8856(03)00137-9
URL : https://hal.archives-ouvertes.fr/hal-00672357

C. Deledalle, J. Salmon, S. Arnak, and . Dalalyan, Image denoising with patch based PCA: local versus global, Procedings of the British Machine Vision Conference 2011, pp.425-455, 2011.
DOI : 10.5244/C.25.25
URL : https://hal.archives-ouvertes.fr/hal-00654289

C. Giraud, Introduction to high-dimensional statistics, 2014.

A. Houdard, C. Bouveyron, and J. Delon, Clustering en haute dimensions pour le débruitage d'image. InXXVIì eme colloque GRETSI, 2017.

A. Houdard, C. Bouveyron, and J. Delon, High-Dimensional Mixture Models For Unsupervised Image Denoising (HDMI). preprint, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01544249

M. Lebrun, A. Buades, and J. M. , A Nonlocal Bayesian Image Denoising Algorithm, SIAM Journal on Imaging Sciences, vol.6, issue.3, pp.1665-1688, 2013.
DOI : 10.1137/120874989

M. Makitalo and A. Foi, A Closed-Form Approximation of the Exact Unbiased Inverse of the Anscombe Variance-Stabilizing Transformation, IEEE Transactions on Image Processing, vol.20, issue.9, pp.2697-2698, 2011.
DOI : 10.1109/TIP.2011.2121085

A. Vladimir, L. A. Mar?enko, and . Pastur, Distribution of eigenvalues for some sets of random matrices, Mathematics of the USSR-Sbornik, p.457, 1967.

D. Mumford and A. Desolneux, Pattern theory: the stochastic analysis of real-world signals, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00713576

E. Ordentlich, G. Seroussi, S. Verdu, M. Weinberger, and T. Weissman, A discrete universal denoiser and its application to binary images, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), p.117, 2003.
DOI : 10.1109/ICIP.2003.1246912

N. Pierazzo, J. Morel, and G. Facciolo, Multi-Scale DCT Denoising, Image Processing On Line, vol.7, pp.288-308, 2017.
DOI : 10.5201/ipol.2017.201
URL : http://www.ipol.im/pub/art/2017/201//article.pdf

J. Salmon and Y. Strozecki, From patches to pixels in Non-Local methods: Weighted-average reprojection, 2010 IEEE International Conference on Image Processing, pp.1929-1932, 2010.
DOI : 10.1109/ICIP.2010.5650780

M. Afonso, . Teodoro, S. Mariana, . Almeida, A. Mário et al., Single-frame image denoising and inpainting using gaussian mixtures, In ICPRAM, issue.2, pp.283-288, 2015.

Y. Wang and J. Morel, SURE Guided Gaussian Mixture Image Denoising, SIAM Journal on Imaging Sciences, vol.6, issue.2, pp.999-1034, 2013.
DOI : 10.1137/120901131
URL : https://hal.archives-ouvertes.fr/hal-00785334

T. Weissman, E. Ordentlich, G. Seroussi, S. Verdú, and M. J. Weinberger, Universal discrete denoising: known channel, IEEE Transactions on Information Theory, vol.51, issue.1, pp.5-28, 2005.
DOI : 10.1109/TIT.2004.839518

J. Yang, X. Liao, X. Yuan, P. Llull, J. David et al., Compressive Sensing by Learning a Gaussian Mixture Model From Measurements, IEEE Transactions on Image Processing, vol.24, issue.1, pp.106-119, 2015.
DOI : 10.1109/TIP.2014.2365720

G. Yu, G. Sapiro, and S. Mallat, Solving inverse problems with piecewise linear estimators: from gaussian mixture models to structured sparsity, IEEE Trans. Image Process, vol.21, issue.5, pp.2481-99, 2012.
DOI : 10.21236/ADA540722
URL : http://www.cmap.polytechnique.fr/%7Emallat/papiers/SSMS_journal_v9.pdf

D. Zoran and Y. Weiss, From learning models of natural image patches to whole image restoration, 2011 International Conference on Computer Vision, pp.479-486, 2011.
DOI : 10.1109/ICCV.2011.6126278
URL : http://www.cs.huji.ac.il/%7Edaniez/EPLLICCVCameraReady.pdf