F. Alter, Y. Matsushita, and X. Tang, An Intensity Similarity Measure in Low-Light Conditions, Lecture Notes in Computer Science, vol.3954, p.267, 2006.
DOI : 10.1007/11744085_21

J. Baxter, Learning internal representations, Proceedings of the eighth annual conference on Computational learning theory , COLT '95, pp.311-320, 1995.
DOI : 10.1145/225298.225336

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

J. Baxter and P. Bartlett, The canonical distortion measure in feature space and 1-nn classification Advances in neural information processing systems 10: proceedings of the 1997 conference, p.245, 1998.

J. Boulanger, C. Kervrann, P. Bouthemy, P. Elbau, J. Sibarita et al., Patch-Based Nonlocal Functional for Denoising Fluorescence Microscopy Image Sequences, IEEE Transactions on Medical Imaging, vol.29, issue.2, pp.442-454, 2010.
DOI : 10.1109/TMI.2009.2033991

URL : https://hal.archives-ouvertes.fr/inria-00541082

Y. Boykov, O. Veksler, and R. Zabih, Markov random fields with efficient approximations, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), pp.648-655, 1998.
DOI : 10.1109/CVPR.1998.698673

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions, pp.1222-1239, 2001.

L. Brown, T. Cai, R. Zhang, L. Zhao, and H. Zhou, The root?unroot algorithm for density estimation as implemented via wavelet block thresholding. Probability theory and, pp.401-433, 2010.

A. Buades, B. Coll, and J. Morel, A Non-Local Algorithm for Image Denoising, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.60-65, 2005.
DOI : 10.1109/CVPR.2005.38

A. Buades, B. Coll, and J. 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

A. Buades, B. Coll, and J. Morel, Non-Local Means Denoising, Image Processing On Line, vol.1, 2009.
DOI : 10.5201/ipol.2011.bcm_nlm

J. Chen, Y. Chen, W. An, Y. Cui, and Y. J. , Nonlocal Filtering for Polarimetric SAR Data: A Pretest Approach. Geoscience and Remote Sensing, IEEE Transactions on, vol.49, issue.5, pp.1744-1754, 2011.

T. Cho, S. Avidan, and W. Freeman, The patch transform, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.1489-1501, 2009.

D. Comaniciu, R. V. Meer, and P. , Kernel-based object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.5, pp.564-577, 2003.
DOI : 10.1109/TPAMI.2003.1195991

A. Criminisi, P. Pérez, and K. Toyama, Region Filling and Object Removal by Exemplar-Based Image Inpainting, IEEE Transactions on Image Processing, vol.13, issue.9, pp.1200-1212, 2004.
DOI : 10.1109/TIP.2004.833105

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, p.2080, 2007.
DOI : 10.1109/TIP.2007.901238

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, A Nonlocal and Shape-Adaptive Transform-Domain Collaborative Filtering, Int. Workshop on Local and Non-Local Approximation in Image Processing, 2008.

C. Deledalle, L. Denis, and F. Tupin, Débruitage Non-Local Itératif fondé sur un Critère de Similarité Probabiliste, the proceedings of GRETSI, 2009.

C. Deledalle, L. Denis, and F. Tupin, Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights, IEEE Transactions on Image Processing, vol.18, issue.12, pp.2661-2672, 2009.
DOI : 10.1109/TIP.2009.2029593

URL : https://hal.archives-ouvertes.fr/ujm-00431266

C. Deledalle, F. Tupin, and L. Denis, Poisson NL means: Unsupervised non local means for Poisson noise, 2010 IEEE International Conference on Image Processing, pp.801-804, 2010.
DOI : 10.1109/ICIP.2010.5653394

URL : https://hal.archives-ouvertes.fr/hal-00957982

C. Deledalle, L. Denis, and F. Tupin, NL-InSAR : Non-Local Interferogram Estimation, IEEE Transaction on Geoscience and Remote Sensing, vol.49, issue.4, 2011.

C. 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/s10851-011-0294-y

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

C. Deledalle, F. Tupin, and L. Denis, Patch similarity under non-gaussian noise Image quilting for texture synthesis and transfer, Image Processing (ICIP) 18th IEEE International Conference on Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pp.341-346, 2001.

M. Elad and A. M. , Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries, IEEE Transactions on Image Processing, vol.15, issue.12, pp.3736-3745, 2006.
DOI : 10.1109/TIP.2006.881969

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

W. Freeman, T. Jones, and E. Pasztor, Example-based super-resolution, IEEE Computer Graphics and Applications, vol.22, issue.2, pp.56-65, 2002.
DOI : 10.1109/38.988747

G. Gilboa and S. Osher, Nonlocal Linear Image Regularization and Supervised Segmentation, Multiscale Modeling & Simulation, vol.6, issue.2, pp.595-630, 2008.
DOI : 10.1137/060669358

J. 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

R. Hartley and A. Zisserman, Multiple view geometry Determining optical flow, Artificial intelligence, vol.642, issue.17, pp.1-3185, 1981.

H. 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

A. Hyvärinen, J. Hurri, and P. Hoyer, Natural Image Statistics: A probabilistic approach to early computational vision Exact optimization for Markov random fields with convex priors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, pp.1333-1336, 2003.
DOI : 10.1007/978-1-84882-491-1

A. Jain, . Nj, V. Katkovnik, A. Foi, K. Egiazarian et al., Fundamentals of digital image processing From local kernel to nonlocal multiple-model image denoising, International journal of computer vision, vol.86, issue.1, pp.1-32, 1989.

S. S. Kay and J. Gabriel, Fundamentals of statistical signal processing Detection theory An invariance property of the generalized likelihood ratio test, Signal Processing Letters IEEE, vol.2, issue.1012, pp.352-355, 1998.

M. Kendall, A. Stuart, C. Griffin, . Ltd, C. London-kervrann et al., The advanced theory of statistics Inference and relationship Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation, International Journal of Computer Vision, vol.2, issue.791, pp.45-69, 1979.

C. Kervrann, J. Boulanger, and P. Coupé, Bayesian Non-local Means Filter, Image Redundancy and Adaptive Dictionaries for Noise Removal, Proceedings of the 1st international conference on Scale space and variational methods in computer vision, pp.520-532, 2007.
DOI : 10.1007/978-3-540-72823-8_45

URL : https://hal.archives-ouvertes.fr/hal-00645444

H. Kim and I. A. Hero, Comparison of GLR and invariant detectors under structured clutter covariance, Image Processing IEEE Transactions on, vol.10, issue.10, pp.1509-1520, 2001.

V. Kwatra, A. Schödl, I. Essa, G. Turk, and A. Bobick, Graphcut textures, ACM Transactions on Graphics, vol.22, issue.3, pp.277-286, 2003.
DOI : 10.1145/882262.882264

E. Lehmann, Optimum Invariant Tests, The Annals of Mathematical Statistics, vol.30, issue.4, pp.881-884, 1959.
DOI : 10.1214/aoms/1177706073

L. Liang, C. Liu, Y. Xu, B. Guo, and H. Shum, Real-time texture synthesis by patch-based sampling, ACM Transactions on Graphics, vol.20, issue.3, pp.127-150, 2001.
DOI : 10.1145/501786.501787

D. Lowe, Robust model-based motion tracking through the integration of search and estimation, International Journal of Computer Vision, vol.2, issue.4, pp.113-122, 1992.
DOI : 10.1007/BF00127170

J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, Non-local sparse models for image restoration. ICCV Mäkitalo M, Foi A (2011) Optimal inversion of the anscombe transformation in low-count poisson image denoising, Image Processing IEEE Transactions on, vol.20, issue.1, pp.99-109, 2009.

M. Mäkitalo, A. Foi, D. Fevralev, and V. Lukin, Denoising of single-look SAR images based on variance stabilization and nonlocal filters, 2010 International Conference on Mathematical Methods in Electromagnetic Theory, 2010.
DOI : 10.1109/MMET.2010.5611418

Y. Matsushita and S. Lin, A Probabilistic Intensity Similarity Measure based on Noise Distributions, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383005

T. Minka, Bayesian Inference, Entropy, and the Multinomial Distribution Distance measures as prior probabilities, 1998.

S. Parrilli, M. Poderico, C. Angelino, G. Scarpa, and L. Verdoliva, A nonlocal approach for SAR image denoising, 2010 IEEE International Geoscience and Remote Sensing Symposium, pp.726-729, 2010.
DOI : 10.1109/IGARSS.2010.5651432

G. Peyré, S. Bougleux, and L. Cohen, Non-local Regularization of Inverse Problems, In: Computer Vision?ECCV, pp.57-68, 2008.
DOI : 10.1007/978-3-540-88690-7_5

J. Salmon, On Two Parameters for Denoising With Non-Local Means, IEEE Signal Processing Letters, vol.17, issue.3, pp.269-272, 2010.
DOI : 10.1109/LSP.2009.2038954

D. Scharstein and R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001), pp.7-42, 2002.
DOI : 10.1109/SMBV.2001.988771

M. Seeger, Covariance kernels from Bayesian generative models Advances in neural information processing systems 14: proceedings of the, p.905, 2001.

T. Teuber, A. Lang, and M. Kocher, A new similarity measure for nonlocal filtering in the presence of multiplicative noise SURE-Based Non-Local Means, IEEE Signal Processing Letters, vol.16, issue.11, pp.973-976, 2009.

M. Varma and A. Zisserman, Texture classification: Are filter banks necessary? In: Computer Vision and Pattern Recognition, Proceedings. 2003 IEEE Computer Society Conference on, p.691, 2003.

H. Xie, L. Pierce, and F. Ulaby, Statistical properties of logarithmically transformed speckle, IEEE Transactions on Geoscience and Remote Sensing, vol.40, issue.3, pp.721-727, 2002.
DOI : 10.1109/TGRS.2002.1000333

P. Yianilos, . Nec-research-institute, . Princeton, X. Zhang, M. Burger et al., Metric learning via normal mixtures Bregmanized nonlocal regularization for deconvolution and sparse reconstruction, SIAM Journal on Imaging Sciences, vol.33253, issue.31, pp.253-276, 1995.

B. Zitova and J. Flusser, Image registration methods: a survey, Image and Vision Computing, vol.21, issue.11, pp.977-1000, 2003.
DOI : 10.1016/S0262-8856(03)00137-9