C. Kervrann and J. Boulanger, Optimal Spatial Adaptation for Patch-Based Image Denoising, IEEE Transactions on Image Processing, vol.15, issue.10, 2006.
DOI : 10.1109/TIP.2006.877529

S. Zhu, Y. Wu, and D. Mumford, Filters, random fields and maximum entropy (frame): Towards a unified theory for texture modeling, International Journal of Computer Vision, vol.27, issue.2, pp.107-126, 1998.
DOI : 10.1023/A:1007925832420

W. Freeman, E. Pasztor, and O. Carmichael, Learning low-level vision, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.25-47, 2000.
DOI : 10.1109/ICCV.1999.790414
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.116.8559

S. Roth and M. Black, Fields of Experts: A Framework for Learning Image Priors, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.26-860, 2005.
DOI : 10.1109/CVPR.2005.160

S. Awate and R. Whitaker, Higher-Order Image Statistics for Unsupervised, Information-Theoretic, Adaptive, Image Filtering, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.44-51, 2005.
DOI : 10.1109/CVPR.2005.176
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.227.8728

G. Gerig, R. Kikinis, O. Jolesz, and F. , Nonlinear anisotropic filtering of MRI data, IEEE Transactions on Medical Imaging, vol.11, issue.2, pp.221-232, 1992.
DOI : 10.1109/42.141646

J. Weickert, B. Ter-haar-romeny, and M. Viergever, Efficient and reliable schemes for nonlinear diffusion filtering, IEEE Transactions on Image Processing, vol.7, issue.3, p.398
DOI : 10.1109/83.661190

S. Keeling, Total variation based convex filters for medical imaging, Applied Mathematics and Computation, vol.139, issue.1, pp.101-119, 2003.
DOI : 10.1016/S0096-3003(02)00171-6

W. Wong, A. Chung, and S. Yu, Trilateral filtering for biomedical images, 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), p.823, 2004.
DOI : 10.1109/ISBI.2004.1398664

R. Nowak, Wavelet-based Rician noise removal for magnetic resonance imaging, IEEE Transactions on Image Processing, vol.8, issue.10, pp.1408-1419, 1999.
DOI : 10.1109/83.791966

J. Wood and K. Johnson, Wavelet packet denoising of magnetic resonance images: Importance of Rician noise at low SNR, Magnetic Resonance in Medicine, vol.3, issue.3, pp.631-635, 1999.
DOI : 10.1002/(SICI)1522-2594(199903)41:3<631::AID-MRM29>3.0.CO;2-Q

S. Zaroubi and G. Goelman, Complex denoising of MR data via wavelet analysis: Application for functional MRI, Magnetic Resonance Imaging, vol.18, issue.1, pp.59-68, 2000.
DOI : 10.1016/S0730-725X(99)00100-9

M. Alexander, R. Baumgartner, A. Summers, C. Windischberger, M. Klarhoefer et al., A wavelet-based method for improving signal-to-noise ratio and contrast in MR images, Magnetic Resonance Imaging, vol.18, issue.2, pp.169-180, 2000.
DOI : 10.1016/S0730-725X(99)00128-9

P. Bao and L. Zhang, Noise reduction for magnetic resonance images via adaptive multiscale products thresholding, IEEE Transactions on Medical Imaging, vol.22, issue.9, pp.1089-1099
DOI : 10.1109/TMI.2003.816958
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.231.3895

A. Ogier, P. Hellier, and C. Barillot, Restoration of 3D medical images with total variation scheme on wavelet domains (TVW) Proceedings of SPIE Medical Imaging, Image Processing, 2006.

Y. Wang and H. Zhou, Total Variation Wavelet-Based Medical Image Denoising, International Journal of Biomedical Imaging, vol.2, issue.1, pp.95-101, 2006.
DOI : 10.1016/j.acha.2005.01.004
URL : http://doi.org/10.1155/ijbi/2006/89095

M. Mcdonnell, Box-filtering techniques Computer Vision, Graphics, and Image Processing, Sept, vol.17, issue.1, pp.65-70, 1981.

L. Yaroslavsky, Editor: Verlag S . Digital Picture Processing -An Introduction, 1985.

J. Lee, Digital image smoothing and the sigma filter Computer Vision, Graphics and Image Processing, pp.255-269, 1983.

R. Coifman and D. Donoho, Translation invariant de-noising . Lecture Notes in Statistics: Wavelets and Statistics, pp.125-150, 1995.
DOI : 10.1007/978-1-4612-2544-7_9
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.125.3682

T. Gasser, L. Sroka, and C. Steinmetz, Residual variance and residual pattern in nonlinear regression, Biometrika, vol.73, issue.3, pp.625-633, 1986.
DOI : 10.1093/biomet/73.3.625

J. Boulanger, C. Kervrann, and P. Bouthemy, Adaptive spatio-temporal restoration for 4D fluoresence microscopic imaging, Int. Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI 05) ' Palm Springs, 2005.
DOI : 10.1007/11566465_110

A. Buades, B. Coll, and J. Morel, Image and movie denoising by nonlocal means . CMLA, Tech Rep, 2006.

M. Mahmoudi and G. Sapiro, Fast image and video denoising via nonlocal means of similar neighborhoods, IEEE Signal Processing Letters, vol.12, issue.12, pp.839-842, 2005.
DOI : 10.1109/LSP.2005.859509

H. Gudbjartsson and S. Patz, The rician distribution of noisy mri data, Magnetic Resonance in Medicine, vol.3, issue.6, pp.910-914, 1995.
DOI : 10.1002/mrm.1910340618

A. Macovski, Noise in MRI, Magnetic Resonance in Medicine, vol.34, issue.3, pp.494-497, 1996.
DOI : 10.1002/mrm.1910360327

S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, An Iterative Regularization Method for Total Variation-Based Image Restoration, Multiscale Modeling & Simulation, vol.4, issue.2, p.460
DOI : 10.1137/040605412