J. Pawley, Handbook of Biological Confocal Microscopy, 1996.

W. H. Richardson, Bayesian-Based Iterative Method of Image Restoration*, Journal of the Optical Society of America, vol.62, issue.1, pp.55-59, 1972.
DOI : 10.1364/JOSA.62.000055

L. B. Lucy, An iterative technique for the rectification of observed distributions, The Astronomical Journal, vol.79, issue.6, pp.745-765, 1974.
DOI : 10.1086/111605

B. Colicchio, E. Maalouf, O. Haeberlé, and A. Dieterlen, Wavelet filtering applied to 3D wide field fluorescence microscopy deconvolution, Proceedings PSIP'07, 2007.

J. Boutet-de-monvel, S. L. Calvez, and M. Ulfendahl, Image Restoration for Confocal Microscopy: Improving the Limits of Deconvolution, with Application to the Visualization of the Mammalian Hearing Organ, Biophysical Journal, vol.80, issue.5, pp.2455-2470, 2001.
DOI : 10.1016/S0006-3495(01)76214-5

F. J. Anscombe, THE TRANSFORMATION OF POISSON, BINOMIAL AND NEGATIVE-BINOMIAL DATA, Biometrika, vol.35, issue.3-4, pp.246-254, 1948.
DOI : 10.1093/biomet/35.3-4.246

M. Fisz, The limiting distribution function of two independent random variables and its statistical application, Colloquium Mathematicum, pp.138-146, 1955.

P. Besbeas, I. De-feis, and T. Sapatinas, A Comparative Simulation Study of Wavelet Shrinkage Estimators for Poisson Counts, International Statistical Review, vol.45, issue.10, pp.209-237, 2004.
DOI : 10.1111/j.1751-5823.2004.tb00234.x

M. Born and E. Wolf, Principles of Optics, p.7, 1999.
DOI : 10.1017/CBO9781139644181

A. Fitzgerrel, E. Dowski, and W. T. Cathey, Defocus transfer function for circularly symmetric pupils, Applied Optics, vol.36, issue.23, pp.5796-5804, 1997.
DOI : 10.1364/AO.36.005796

B. Zhang, J. Fadili, J. Starck, and J. Olivo-marin, Multiscale Variance-Stabilizing Transform for Mixed-Poisson-Gaussian Processes and its Applications in Bioimaging, 2007 IEEE International Conference on Image Processing, 2007.
DOI : 10.1109/ICIP.2007.4379564

G. Van-kempen, L. Van, and . Vliet, The influence of the regularization parameter and the first estimate on the performance of Tikhonov regularized non-linear image restoration algorithms, Journal of Microscopy, vol.198, issue.1, pp.63-75, 2000.
DOI : 10.1046/j.1365-2818.2000.00671.x

N. Dey, L. Blanc-féraud, C. Zimmer, Z. Kam, J. Olivo-marin et al., A deconvolution method for confocal microscopy with total variation regularization, 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821), 2004.
DOI : 10.1109/ISBI.2004.1398765

N. Dey, L. Blanc-féraud, C. Zimmer, Z. Kam, P. Roux et al., Richardson???Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution, Microscopy Research and Technique, vol.59, issue.4, pp.260-266, 2006.
DOI : 10.1002/jemt.20294

F. Rooms, W. Philips, and D. S. Lidke, Simultaneous degradation estimation and restoration of confocal images and performance evaluation by colocalization analysis, Journal of Microscopy, vol.178, issue.1, pp.22-36, 2005.
DOI : 10.1046/j.1365-2818.1999.00421.x

J. G. Mcnally, T. Karpova, J. Cooper, and J. Conchello, Three-Dimensional Imaging by Deconvolution Microscopy, Methods, vol.19, issue.3, pp.373-385, 1999.
DOI : 10.1006/meth.1999.0873

J. Kalifa, S. Mallat, and B. Rougé, Deconvolution by thresholding in mirror wavelet bases, IEEE Transactions on Image Processing, vol.12, issue.4, pp.446-457, 2003.
DOI : 10.1109/TIP.2003.810592

J. Bect, L. Blanc-féraud, G. Aubert, and A. Chambolle, A l 1-Unified Variational Framework for Image Restoration, Proc. European Conference on Computer Vision (ECCV), pp.1-13, 2004.
DOI : 10.1007/978-3-540-24673-2_1

URL : https://hal.archives-ouvertes.fr/hal-00217251

I. Daubechies, M. Defrise, and C. Mol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint, Communications on Pure and Applied Mathematics, vol.58, issue.11, pp.1413-1457, 2004.
DOI : 10.1002/cpa.20042

P. L. Combettes and V. R. Wajs, Signal Recovery by Proximal Forward-Backward Splitting, Multiscale Modeling & Simulation, vol.4, issue.4, pp.1168-1200, 2005.
DOI : 10.1137/050626090

URL : https://hal.archives-ouvertes.fr/hal-00017649

I. Csiszár, Why Least Squares and Maximum Entropy? An Axiomatic Approach to Inference for Linear Inverse Problems, The Annals of Statistics, vol.19, issue.4, pp.2032-2066, 1991.
DOI : 10.1214/aos/1176348385

S. Mallat, A wavelet tour of signal processing, 1998.

J. I. Donoho and D. L. , Adapting to Unknown Smoothness via Wavelet Shrinkage, Journal of the American Statistical Association, vol.31, issue.432, pp.1200-1224, 1995.
DOI : 10.1080/01621459.1979.10481038

N. Kingsbury, The dual-tree complex wavelet transform : a new efficient tool for image restoration and enhancement, Proc. of EUSIPCO, pp.319-322, 1998.

C. Chaux, L. Duval, and J. Pesquet, Image analysis using a dual-tree M-band wavelet transform, IEEE Transactions on Image Processing, vol.15, issue.8, pp.2397-2412, 2006.
DOI : 10.1109/TIP.2006.875178

URL : https://hal.archives-ouvertes.fr/hal-01330599

A. Jalobeanu, L. Blanc-féraud, and J. Zerubia, Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method, Pattern Recognition, vol.35, issue.2, p.341352, 2002.
DOI : 10.1016/S0031-3203(00)00178-3