H. Akaike, Information theory and an extension of the maximum likelihood principle, Second International Symposium on Information Theory, pp.267-281, 1973.

T. Blu and F. Luisier, The SURE-LET Approach to Image Denoising, IEEE Transactions on Image Processing, vol.16, issue.11, pp.2778-2786, 2007.
DOI : 10.1109/TIP.2007.906002

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.71.1201

L. D. Brown, Fundamentals of statistical exponential families with applications in statistical decision theory. Lecture Notes?Monograph Series i, p.279, 1986.

A. Buades, B. Coll, and J. M. Morel, A Review of Image Denoising Algorithms, with a New One, Multiscale Modeling & Simulation, vol.4, issue.2, p.490, 2005.
DOI : 10.1137/040616024

URL : https://hal.archives-ouvertes.fr/hal-00271141

T. T. Cai and H. H. Zhou, A data-driven block thresholding approach to wavelet estimation, The Annals of Statistics, vol.37, issue.2, pp.569-595, 2009.
DOI : 10.1214/07-AOS538

URL : http://arxiv.org/abs/0903.5147

C. Chaux, L. Duval, A. Benazza-benyahia, and J. Pesquet, A Nonlinear Stein-Based Estimator for Multichannel Image Denoising, IEEE Transactions on Signal Processing, vol.56, issue.8, pp.3855-3870, 2008.
DOI : 10.1109/TSP.2008.921757

URL : https://hal.archives-ouvertes.fr/hal-00617318

L. H. Chen, Poisson approximation for dependent trials. The Annals of Probability 3, pp.534-545, 1975.
DOI : 10.1214/aop/1176996359

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. A. Deledalle, V. Duval, and J. Salmon, Non-local Methods with Shape-Adaptive Patches (NLM-SAP), Journal of Mathematical Imaging and Vision, vol.13, issue.4, pp.1-18, 2011.
DOI : 10.1007/978-3-642-81929-2

URL : https://hal.archives-ouvertes.fr/hal-00536723

C. Deledalle, S. Vaiter, J. Fadili, and G. Peyré, Stein Unbiased GrAdient estimator of the Risk (SUGAR) for Multiple Parameter Selection, SIAM Journal on Imaging Sciences, vol.7, issue.4, pp.2448-2487, 2014.
DOI : 10.1137/140968045

URL : https://hal.archives-ouvertes.fr/hal-00987295

D. L. Donoho and I. M. Johnstone, Adapting to Unknown Smoothness via Wavelet Shrinkage, Journal of the American Statistical Association, vol.31, issue.432, pp.1200-1224, 1995.
DOI : 10.1007/978-3-0346-0416-1

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.161.8697

V. Duval, J. Aujol, and Y. Gousseau, A Bias-Variance Approach for the Nonlocal Means, SIAM Journal on Imaging Sciences, vol.4, issue.2, pp.760-788, 2011.
DOI : 10.1137/100790902

URL : https://hal.archives-ouvertes.fr/hal-00947885

B. Efron, How Biased is the Apparent Error Rate of a Prediction Rule?, Journal of the American Statistical Association, vol.39, issue.394, pp.461-470, 1986.
DOI : 10.2307/2288636

Y. C. Eldar, Generalized SURE for Exponential Families: Applications to Regularization, IEEE Transactions on Signal Processing, vol.57, issue.2, pp.471-481, 2009.
DOI : 10.1109/TSP.2008.2008212

URL : http://arxiv.org/abs/0804.3010

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

L. C. Evans and R. F. Gariepy, Measure theory and fine properties of functions, 1992.

E. I. George, F. Liang, and X. Xu, Improved minimax predictive densities under Kullback-Leibler loss. The Annals of Statistics, pp.78-91, 2006.
DOI : 10.1214/009053606000000155

URL : http://arxiv.org/abs/math/0605432

D. Gilbarg and N. S. Trudinger, Elliptic Partial Differential Equations of Second Order, 1998.
DOI : 10.1007/978-3-642-96379-7

A. Girard, A fast ?Monte-Carlo cross-validation? procedure for large least squares problems with noisy data, Numerische Mathematik, vol.14, issue.1, pp.1-23, 1989.
DOI : 10.1007/BF01390708

G. H. Golub, M. Heath, and G. Wahba, Generalized crossvalidation as a method for choosing a good ridge parameter, 1979.
DOI : 10.2307/1268518

J. W. Goodman, Some fundamental properties of speckle*, Journal of the Optical Society of America, vol.66, issue.11, pp.1145-1150, 1976.
DOI : 10.1364/JOSA.66.001145

P. Hall, On Kullback-Leibler loss and density estimation. The Annals of Statistics, pp.1491-1519, 1987.
DOI : 10.1214/aos/1176350606

M. Hamada and E. A. Valdez, CAPM and Option Pricing With Elliptically Contoured Distributions, Journal of Risk & Insurance, vol.7, issue.2, pp.387-409, 2008.
DOI : 10.2307/1911158

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.573.4715

J. Hannig and T. Lee, Kernel smoothing of periodograms under Kullback???Leibler discrepancy, Signal Processing, vol.84, issue.7, pp.1255-1266, 2004.
DOI : 10.1016/j.sigpro.2004.04.007

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.495.4330

J. Hannig and T. Lee, On Poisson signal estimation under Kullback???Leibler discrepancy and squared risk, Journal of Statistical Planning and Inference, vol.136, issue.3, pp.882-908, 2006.
DOI : 10.1016/j.jspi.2004.08.012

H. M. Hudson, A Natural Identity for Exponential Families with Applications in Multiparameter Estimation, The Annals of Statistics, vol.6, issue.3, pp.473-484, 1978.
DOI : 10.1214/aos/1176344194

S. Kullback and R. A. Leibler, On information and sufficiency. The Annals of Mathematical Statistics, pp.79-86, 1951.
DOI : 10.1214/aoms/1177729694

Z. Landsman and J. Ne?lehová, Stein's Lemma for elliptical random vectors, Journal of Multivariate Analysis, vol.99, issue.5, pp.912-927, 2008.
DOI : 10.1016/j.jmva.2007.05.006

URL : http://doi.org/10.1016/j.jmva.2007.05.006

E. Lehmann, Theory of point estimation, 1983.

K. Li, From Stein's Unbiased Risk Estimates to the Method of Generalized Cross Validation, The Annals of Statistics, vol.13, issue.4, pp.1352-1377, 1985.
DOI : 10.1214/aos/1176349742

F. Luisier, The SURE-LET approach to image denoising PhD thesis, 2010.

F. Luisier, T. Blu, and M. Unser, SURE-LET for Orthonormal Wavelet-Domain Video Denoising, IEEE Transactions on Circuits and Systems for Video Technology, vol.20, issue.6, pp.913-919, 2010.
DOI : 10.1109/TCSVT.2010.2045819

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.190.4834

F. Luisier, T. Blu, and P. J. Wolfe, A CURE for Noisy Magnetic Resonance Images: Chi-Square Unbiased Risk Estimation, IEEE Transactions on Image Processing, vol.21, issue.8, pp.3454-3466, 2012.
DOI : 10.1109/TIP.2012.2191565

URL : http://arxiv.org/abs/1106.2848

J. Lv and J. S. Liu, Model selection principles in misspecified models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.55, issue.1, pp.141-167, 2014.
DOI : 10.1016/j.csda.2011.04.016

URL : http://arxiv.org/abs/1005.5483

C. L. Mallows, Some Comments on Cp, Technometrics, vol.15, pp.661-675, 1973.

C. N. Morris, Natural exponential families with quadratic variance functions. The Annals of Statistics, pp.65-80, 1982.
DOI : 10.1214/aos/1176345690

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

S. Ramani, T. Blu, and M. Unser, Monte-Carlo Sure: A Black-Box Optimization of Regularization Parameters for General Denoising Algorithms, IEEE Transactions on Image Processing, vol.17, issue.9, pp.1540-1554, 2008.
DOI : 10.1109/TIP.2008.2001404

S. Ramani, Z. Liu, J. Rosen, J. Nielsen, and J. A. Fessler, Regularization Parameter Selection for Nonlinear Iterative Image Restoration and MRI Reconstruction Using GCV and SURE-Based Methods, IEEE Transactions on Image Processing, vol.21, issue.8, pp.3659-3672, 2012.
DOI : 10.1109/TIP.2012.2195015

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3411925

M. Raphan and E. P. Simoncelli, Learning to be Bayesian without supervision, Advances in Neural Inf. Process. Syst. (NIPS), pp.1145-1152, 2007.

P. Rigollet, Kullback???Leibler aggregation and misspecified generalized linear models, The Annals of Statistics, vol.40, issue.2, pp.639-665, 2012.
DOI : 10.1214/11-AOS961SUPP

URL : http://arxiv.org/abs/0911.2919

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

C. M. Stein, Estimation of the Mean of a Multivariate Normal Distribution, The Annals of Statistics, vol.9, issue.6, pp.1135-1151, 1981.
DOI : 10.1214/aos/1176345632

R. Tibshirani, Regression shrinkage and selection via the Lasso, J. of the Royal Statistical Society. Series B. Methodological, vol.58, pp.267-288, 1996.
DOI : 10.1111/j.1467-9868.2011.00771.x

D. Van-de-ville and M. Kocher, SURE-Based Non-Local Means, IEEE Signal Processing Letters, vol.16, issue.11, pp.973-976, 2009.
DOI : 10.1109/LSP.2009.2027669

D. Van-de-ville and M. Kocher, Non-local means with dimensionality reduction and SURE-based parameter selection, IEEE Trans. Image Process, vol.9, pp.2683-2690, 2011.

T. Yanagimoto, The Kullback-Leibler risk of the Stein estimator and the conditional MLE, Annals of the Institute of Statistical Mathematics, vol.29, issue.1, pp.29-41, 1994.
DOI : 10.1007/978-1-4612-0971-3