]. F. Aghdasi-1996, R. K. Aghdasi, and . Ward, Reduction of boundary artifacts in image restoration, IEEE Transactions on Image Processing, vol.5, issue.4, pp.611-618, 1996.
DOI : 10.1109/83.491337

]. M. Arigovindan, J. Shaevitz, J. Mcgowan, J. W. Sedat, and D. A. Agard, A Parallel Product-Convolution approach for representing the depth varying Point Spread Functions in 3D widefield microscopy based on principal component analysis, Optics Express, vol.18, issue.7, pp.6461-6476, 2010.
DOI : 10.1364/OE.18.006461

]. M. Aubailly, M. C. Roggemann, and T. J. Schulz, Approach for reconstructing anisoplanatic adaptive optics images, Applied Optics, vol.46, issue.24, pp.6055-6063, 2007.
DOI : 10.1364/AO.46.006055

]. G. Aubert and P. Kornprobst, Mathematical problems in image processing : partial differential equations and the calculus of variations, 2006.

]. G. Ayers and J. C. Dainty, Iterative blind deconvolution method and its applications, Optics Letters, vol.13, issue.7, pp.547-549, 1988.
DOI : 10.1364/OL.13.000547

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.466.6607

]. L. Bar, N. Sochen, and N. Kiryati, Semi-blind image restoration via Mumford-Shah regularization, IEEE Transactions on Image Processing, vol.15, issue.2, pp.483-493, 2006.
DOI : 10.1109/TIP.2005.863120

]. L. Bar, N. Sochen, and N. Kiryati, Restoration of images with piecewise spacevariant blur. Scale Space and Variational Methods in Computer Vision, pp.533-544, 2007.

]. J. Bardsley and C. R. Vogel, A Nonnegatively Constrained Convex Programming Method for Image Reconstruction, SIAM Journal on Scientific Computing, vol.25, issue.4, pp.1326-1343, 2004.
DOI : 10.1137/S1064827502410451

]. J. Bardsley, S. Jefferies, J. Nagy, and R. Plemmons, A computational method for the restoration of images with an unknown, spatially-varying blur, Optics Express, vol.14, issue.5, pp.1767-1782, 2006.
DOI : 10.1364/OE.14.001767

]. J. Bardsley, S. Jefferies, J. Nagy, and R. Plemmons, A computational method for the restoration of images with an unknown, spatially-varying blur, Optics Express, vol.14, issue.5, pp.1767-1782, 2006.
DOI : 10.1364/OE.14.001767

]. J. Bardsley and J. Goldes, Regularization parameter selection methods for ill-posed Poisson maximum likelihood estimation, Inverse Problems, vol.25, issue.9, p.95005, 2009.
DOI : 10.1088/0266-5611/25/9/095005

]. J. Barzilai and J. M. Borwein, Two-Point Step Size Gradient Methods, IMA Journal of Numerical Analysis, vol.8, issue.1, pp.141-148, 1988.
DOI : 10.1093/imanum/8.1.141

]. J. Bect, L. Blanc-féraud, G. Aubert, and A. Chambolle, A L1-unified variational framework for image restoration, pp.1-13, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00217251

]. M. Bertero and P. Boccacci, Introduction to Inverse Problems in Imaging, 1998.
DOI : 10.1887/0750304359

]. M. Bertero and P. Boccacci, Introduction to inverse problems in imaging, 1998.
DOI : 10.1887/0750304359

]. M. Bertero, C. De-mol, and E. Pike, Linear inverse problems with discrete data: II. Stability and regularisation, Inverse Problems, vol.4, issue.3, p.573, 1999.
DOI : 10.1088/0266-5611/4/3/004

]. J. Biemond, R. L. Lagendijk, and R. M. Mersereau, Iterative methods for image deblurring, Proceedings of the IEEE, pp.856-883, 1990.
DOI : 10.1109/5.53403

]. L. Blanco and . Mugnier, Marginal blind deconvolution of adaptive optics retinal images, Optics Express, vol.19, issue.23, pp.23227-23239, 2011.
DOI : 10.1364/OE.19.023227

M. Blume, D. Zikic, W. Wein, and N. Navab, A New and General Method for Blind Shift-Variant Deconvolution of Biomedical Images, Medical Image Computing and Computer-Assisted Intervention, vol.4791, pp.743-750, 2007.
DOI : 10.1007/978-3-540-75757-3_90

. Af-boden, R. Dc-redding, J. Hanisch, and . Mo, Massively parallel spatially variant maximum-likelihood restoration of Hubble Space Telescope imagery, Journal of the Optical Society of America A, vol.13, issue.7, pp.1537-1545, 1996.
DOI : 10.1364/JOSAA.13.001537

]. J. Bolte, J. Combettes, and . Pesquet, Alternating proximal algorithm for blind image recovery, 2010 IEEE International Conference on Image Processing, 2010.
DOI : 10.1109/ICIP.2010.5652173

URL : https://hal.archives-ouvertes.fr/hal-00844115

S. Bonettini, R. Zanella, and L. Zanni, A scaled gradient projection method for constrained image deblurring, Inverse Problems, vol.25, issue.1, pp.15002-15050, 2009.
DOI : 10.1088/0266-5611/25/1/015002

]. J. Bonnans, J. C. Gilbert, C. Lemaréchal, and C. A. Sagastizábal, Numerical optimization : theoretical and practical aspects, pp.46-47, 2006.
DOI : 10.1007/978-3-662-05078-1

]. M. Booth, T. Neil, and . Wilson, Aberration correction for confocal imaging in refractive-index-mismatched media, Journal of Microscopy, vol.192, issue.2, pp.90-98, 1998.
DOI : 10.1111/j.1365-2818.1998.99999.x

]. M. Booth, Adaptive optics in microscopy, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.82, issue.18, pp.2829-2843, 2007.
DOI : 10.1364/OL.31.002783

]. M. Born and E. Wolf, Principles of optics, 1959.
DOI : 10.1017/CBO9781139644181

]. M. Born and E. Wolf, Principles of optics : electromagnetic theory of propagation , interference and diffraction of light, 1999.
DOI : 10.1017/CBO9781139644181

]. M. Born and E. Wolf, Principles of optics, 2003.
DOI : 10.1017/CBO9781139644181

]. S. Boyd and L. Vandenberghe, Convex optimization, 2004.

]. P. Campisi and K. Egiazarian, Blind image deconvolution : theory and applications, CRC, 2007.
DOI : 10.1201/9781420007299

]. M. Carlavan and L. Blanc-féraud, Sparse Poisson Noisy Image Deblurring, IEEE Transactions on Image Processing, vol.21, issue.4, pp.1-1, 2011.
DOI : 10.1109/TIP.2011.2175934

URL : https://hal.archives-ouvertes.fr/inria-00634896

]. A. Chambolle and T. Pock, A First-Order Primal-Dual Algorithm for Convex Problems with??Applications to Imaging, Journal of Mathematical Imaging and Vision, vol.60, issue.5, pp.120-145, 2011.
DOI : 10.1007/s10851-010-0251-1

URL : https://hal.archives-ouvertes.fr/hal-00490826

]. T. Chan and C. K. Wong, Convergence of the alternating minimization algorithm for blind deconvolution, Linear Algebra and its Applications, vol.316, issue.1-3, pp.259-285, 2000.
DOI : 10.1016/S0024-3795(00)00141-5

]. C. Chaux, L. Blanc-féraud, and J. Zerubia, Wavelet-based restoration methods: application to 3D confocal microscopy images, Wavelets XII, 2007.
DOI : 10.1117/12.731438

URL : https://hal.archives-ouvertes.fr/hal-00733457

]. S. Cho and S. Lee, Fast motion deblurring, In ACM Transactions on Graphics, vol.28, p.145, 2009.

]. R. Cole, T. Jinadasa, and C. M. Brown, Measuring and interpreting point spread functions to determine confocal microscope resolution and ensure quality control, Nature Protocols, vol.46, issue.12, pp.1929-1941, 2011.
DOI : 10.1247/csf.27.335

]. G. Cresci, A. Davies, . Baker, and . Lehnert, Accounting for the anisoplanatic point spread function in deep wide-field adaptive optics images, Astronomy and Astrophysics, vol.438, issue.2, pp.757-767, 2005.
DOI : 10.1051/0004-6361:20052890

]. Y. Dai and R. Fletcher, New algorithms for singly linearly constrained quadratic programs subject to lower and upper bounds, Mathematical Programming, pp.403-421, 2006.
DOI : 10.1007/s10107-005-0595-2

]. I. Daubechies, M. Defrise, and C. D. 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

D. Meyer and 2. Meyer, ContributionàContribution`Contributionà l'amélioration des outils de restauration d'images et de caractérisation de l'instrument en microscopie 3D par fluorescence, 2008.

D. Mol and 1. Mol, A critical survey of regularized inversion methods, Inverse Problems in Scattering and Imaging, pp.345-369

]. A. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (Methodological), pp.1-38, 1977.

]. L. Denis, E. Thiébaut, and F. Soulez, Fast model of space-variant blurring and its application to deconvolution in astronomy, 2011 18th IEEE International Conference on Image Processing, 2011.
DOI : 10.1109/ICIP.2011.6116257

URL : https://hal.archives-ouvertes.fr/hal-00618696

]. L. Desbat and D. Girard, The ???Minimum Reconstruction Error??? Choice of Regularization Parameters: Some More Efficient Methods and Their Application to Deconvolution Problems, SIAM Journal on Scientific Computing, vol.16, issue.6, pp.1387-1403, 1995.
DOI : 10.1137/0916080

L. Dey, C. Blanc-féraud, Z. Zimmer, P. Kam, J. C. 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. Dupé, J. M. Fadili, and J. L. Starck, A Proximal Iteration for Deconvolving Poisson Noisy Images Using Sparse Representations, IEEE Transactions on Image Processing, vol.18, issue.2, pp.310-321, 2009.
DOI : 10.1109/TIP.2008.2008223

]. A. Egner and S. W. Hell, Aberrations in confocal and multi-photon fluorescence microscopy induced by refractive index mismatch. Handbook of Biological Confocal Microscopy, pp.404-413, 2006.

]. H. Engl, M. Hanke, and A. Neubauer, Regularization of inverse problems, 1996.

]. J. Fienup, Phase retrieval algorithms: a comparison, Applied Optics, vol.21, issue.15, pp.2758-2769, 1982.
DOI : 10.1364/AO.21.002758

M. A. Figueiredo and R. D. Nowak, An EM algorithm for wavelet-based image restoration, IEEE Transactions on Image Processing, vol.12, issue.8, pp.906-916, 2003.
DOI : 10.1109/TIP.2003.814255

]. M. Fornasier, A. Langer, and C. B. Schönlieb, A convergent overlapping domain decomposition method for total variation minimization, Numerische Mathematik, vol.31, issue.3, pp.1-41, 2009.
DOI : 10.1007/s00211-010-0314-7

]. M. Fornasier and C. B. Schönlieb, Subspace Correction Methods for Total Variation and $\ell_1$-Minimization, SIAM Journal on Numerical Analysis, vol.47, issue.5, pp.3397-2009, 2009.
DOI : 10.1137/070710779

]. G. Frassoldati, L. Zanni, and G. Zanghirati, New adaptive stepsize selections in gradient methods, Journal of industrial and management optimization, vol.4, issue.2, p.299, 2008.

F. Gibson, S. Frisken-gibson, and F. Lanni, Experimental test of an analytical model of aberration in an oil-immersion objective lens used in three-dimensional light microscopy, Journal of the Optical Society of America A, vol.8, issue.10, pp.1601-1613, 1991.
DOI : 10.1364/JOSAA.8.001601

]. J. Fujimoto and D. Farkas, Biomedical Optical Imaging, Journal of Biomedical Optics, vol.15, issue.5, 2009.
DOI : 10.1117/1.3490919

]. N. Galatsanos and A. K. Katsaggelos, Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation, IEEE Transactions on Image Processing, vol.1, issue.3, pp.322-336, 1992.
DOI : 10.1109/83.148606

]. S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. Pattern Analysis and Machine Intelligence, IEEE Transactions, issue.6, pp.721-741, 1984.

]. S. Geman, K. M. Manbeck, and D. E. Mcclure, A comprehensive statistical model for single-photon emission tomography, 1993.

]. S. Gibson and F. Lanni, Diffraction by a circular aperture as a model for three-dimensional optical microscopy, Journal of the Optical Society of America A, vol.6, issue.9, pp.1357-1367, 1989.
DOI : 10.1364/JOSAA.6.001357

]. E. Gilad and J. Hardenberg, A fast algorithm for convolution integrals with space and time variant kernels, Journal of Computational Physics, vol.216, issue.1, pp.326-336, 2006.
DOI : 10.1016/j.jcp.2005.12.003

]. P. Green, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, vol.82, issue.4, pp.711-732, 1995.
DOI : 10.1093/biomet/82.4.711

]. A. Griffa-2010a, N. Griffa, D. Garin, and . Sage, Comparison of Deconvolution Software in 3D Microscopy : A User Point of View?Part 1, G.I.T. Imaging & Microscopy, vol.12, issue.3, pp.43-45

]. A. Griffa-2010b, N. Griffa, D. Garin, and . Sage, Comparison of Deconvolution Software in 3D Microscopy : A User Point of View?Part 2, G.I.T. Imaging & Microscopy, vol.12, issue.3, pp.41-43

. Guo, . Lee, and . Teo, Blind restoration of images degraded by space-variant blurs using iterative algorithms for both blur identification and image restoration, Image and Vision Computing, vol.15, issue.5, pp.399-410, 1996.
DOI : 10.1016/S0262-8856(96)01143-2

]. O. Haeberle and B. Simon, The point spread function of fluorescence microscopes imaging through a layered or an inhomogeneous medium, Biophotonics Micro- and Nano-Imaging, p.11, 2004.
DOI : 10.1117/12.545792

]. O. Haeberlé, K. Belkebir, H. Giovaninni, and A. Sentenac, Tomographic diffractive microscopy: basics, techniques and perspectives, Journal of Modern Optics, vol.92, issue.9, pp.686-699, 2010.
DOI : 10.1126/science.1127344

N. Hajlaoui, C. Chaux, G. Perrin, F. Falzon, and A. Benazza-benyahia, Satellite image restoration in the context of a spatially varying point spread function, Journal of the Optical Society of America A, vol.27, issue.6, pp.1473-1481, 2010.
DOI : 10.1364/JOSAA.27.001473

URL : https://hal.archives-ouvertes.fr/hal-00714260

]. P. Hansen, Analysis of Discrete Ill-Posed Problems by Means of the L-Curve, SIAM Review, vol.34, issue.4, pp.561-580, 1992.
DOI : 10.1137/1034115

]. B. Hanser, M. G. Gustafsson, D. A. Agard, and J. W. Sedat, Phase retrieval for high-numerical-aperture optical systems, Optics Letters, vol.28, issue.10, pp.801-803, 2003.
DOI : 10.1364/OL.28.000801

]. S. Harmeling, M. Hirsch, and B. Schölkopf, Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake, Advances in Neural Information Processing Systems (NIPS), pp.2010-94, 2010.

]. Herberich, R. Windoffer, R. Leube, and . Aach, Signal and Noise Modeling in Confocal Laser Scanning Fluorescence Microscopy, Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.381-388, 2012.
DOI : 10.1007/978-3-642-33415-3_47

]. Herberich, R. Windoffer, R. Leube, and . Aach, Signal and Noise Modeling in Confocal Laser Scanning Fluorescence Microscopy, Medical Image Computing and Computer-Assisted Intervention -MICCAI 2012, pp.381-388, 2012.
DOI : 10.1007/978-3-642-33415-3_47

]. M. Hirsch, S. Sra, B. Scholkopf, and S. Harmeling, Efficient filter flow for space-variant multiframe blind deconvolution, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.607-614, 2010.
DOI : 10.1109/CVPR.2010.5540158

]. S. Hiware, P. Porwal, R. Velmurugan, and S. Chaudhuri, Modeling of PSF for refractive index variation in fluorescence microscopy, 2011 18th IEEE International Conference on Image Processing, pp.2011-2034, 2011.
DOI : 10.1109/ICIP.2011.6115879

]. H. Hopkins, The Frequency Response of a Defocused Optical System, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.231, issue.1184, pp.91-103, 1955.
DOI : 10.1098/rspa.1955.0158

]. M. Ichir and A. Mohammad-djafari, Hidden Markov models for wavelet-based blind source separation, IEEE Transactions on Image Processing, vol.15, issue.7, pp.1887-1899, 2006.
DOI : 10.1109/TIP.2006.877068

URL : https://hal.archives-ouvertes.fr/hal-00449843

]. S. Inoué, Foundations of confocal scanned imaging in light microscopy. Handbook of biological confocal microscopy, pp.1-19, 2006.

]. H. Jacobsen, P. Hänninen, and E. Soini, Refractive-index-induced aberrations in two-photon confocal fluorescence microscopy, Journal of Microscopy, vol.90, issue.3, pp.226-230, 2011.
DOI : 10.1111/j.1365-2818.1994.tb03519.x

]. E. Jacquelin, A. Bennani, and P. Hamelin, Force reconstruction: analysis and regularization of a deconvolution problem, Journal of Sound and Vibration, vol.265, issue.1, pp.81-107, 2003.
DOI : 10.1016/S0022-460X(02)01441-4

]. A. Jain, Fundamentals of digital image processing, 1989.

]. 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, pp.341-352, 2002.
DOI : 10.1016/S0031-3203(00)00178-3

]. A. Jezierska, C. Chaux, J. Pesquet, and H. Talbot, An EM approach for Poisson-Gaussian noise modeling, European Signal Processing Conference (EUSIPCO), pp.2244-2248, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00733633

Z. Kam, B. Hanser, . Mgl-gustafsson, J. Da-agard, and . Sedat, Computational adaptive optics for live three-dimensional biological imaging, Proceedings of the National Academy of Sciences, pp.3790-3795, 2001.
DOI : 10.1073/pnas.071275698

]. A. Katsaggelos, T. Schroeder, . Kohonen, and . Huang, Digital image restoration, 1991.
DOI : 10.1007/978-3-642-58216-5

]. T. Kenig, Z. Kam, and A. Feuer, Blind image deconvolution using machine learning for three-dimensional microscopy. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.32, issue.12, pp.2191-2204, 2010.

]. K. König, Multiphoton microscopy in life sciences, Journal of Microscopy, vol.200, issue.2, pp.83-104, 2001.
DOI : 10.1046/j.1365-2818.2000.00738.x

M. Sta´ndosta´ndo and . Rudnicki, Regularization parameter selection in discrete ill-posed problems-the use of the U-curve, International Journal of Applied Mathematics and Computer Science, vol.17, issue.2, pp.157-164, 2007.

]. D. Krishnan and R. Fergus, Fast image deconvolution using hyperlaplacian priors, Advances in Neural Information Processing Systems, pp.1-9, 2009.

]. D. Kundur and D. Hatzinakos, A novel blind deconvolution scheme for image restoration using recursive filtering, IEEE Transactions on Signal Processing, vol.46, issue.2, pp.375-390, 1998.
DOI : 10.1109/78.655423

]. J. Lakowicz, Principles of fluorescence spectroscopy, 2006.
DOI : 10.1007/978-0-387-46312-4

]. E. Lam and J. W. Goodman, Iterative blind image deconvolution in space and frequency domains, Proc. SPIE, pp.70-77, 1999.

]. L. Landweber, An Iteration Formula for Fredholm Integral Equations of the First Kind, American Journal of Mathematics, vol.73, issue.3, pp.615-624, 1951.
DOI : 10.2307/2372313

L. Bates, Automatic multidimensional deconvolution, JOSA A, vol.4, issue.1, pp.180-188, 1987.

]. H. Lantéri, M. Roche, and C. Aime, Penalized maximum likelihood image restoration with positivity constraints : multiplicative algorithms Inverse problems, pp.1397-1419, 2002.

]. T. Le, R. Chartrand, and T. J. Asaki, A Variational Approach to Reconstructing Images Corrupted by Poisson Noise, Journal of Mathematical Imaging and Vision, vol.3, issue.3, pp.257-263, 2007.
DOI : 10.1007/s10851-007-0652-y

]. A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, Understanding and evaluating blind deconvolution algorithms, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.1964-1971, 2009.
DOI : 10.1109/CVPR.2009.5206815

]. A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, Efficient marginal likelihood optimization in blind deconvolution, CVPR 2011, pp.2657-2664, 2011.
DOI : 10.1109/CVPR.2011.5995308

]. F. Luisier, T. Blu, and M. Unser, Image Denoising in Mixed Poisson–Gaussian Noise, IEEE Transactions on Image Processing, vol.20, issue.3, pp.696-708, 2011.
DOI : 10.1109/TIP.2010.2073477

]. E. Maalouf, Contribution to fluorescence microscopy, 3D thick samples deconvolution and depth-variant PSF, pp.62-64, 2010.
URL : https://hal.archives-ouvertes.fr/tel-00594247

N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, vol.21, issue.6, pp.1087-52, 1953.
DOI : 10.1063/1.1699114

]. M. Minsky, Memoir on inventing the confocal scanning microscope, Scanning, vol.10, issue.4, pp.128-138, 1988.
DOI : 10.1002/sca.4950100403

]. J. More, The Levenberg-Marquardt algorithm : implementation and theory. Numerical analysis, pp.105-116, 1978.

]. L. Mugnier, T. Fusco, and J. M. Conan, Mistral: a myopic edge-preserving image restoration method, with application to astronomical adaptive-optics-corrected long-exposure images, Journal of the Optical Society of America A, vol.21, issue.10, pp.1841-1854, 2004.
DOI : 10.1364/JOSAA.21.001841

]. J. Nagy, R. J. Plemmons, and T. C. Torgersen, Iterative image restoration using approximate inverse preconditioning, IEEE Transactions on Image Processing, vol.5, issue.7, pp.1151-1162, 1996.
DOI : 10.1109/83.502394

]. J. Nagy and D. P. Leary, Restoring Images Degraded by Spatially Variant Blur, SIAM Journal on Scientific Computing, vol.19, issue.4, pp.1063-1082, 1998.
DOI : 10.1137/S106482759528507X

]. M. Ng, P. Weiss, and X. M. Yuan, Solving Constrained Total-variation Image Restoration and Reconstruction Problems via Alternating Direction Methods, SIAM Journal on Scientific Computing, vol.32, issue.5, pp.2710-2736, 2010.
DOI : 10.1137/090774823

]. J. Nocedal and S. J. Wright, Numerical optimization, 1999.
DOI : 10.1007/b98874

F. Orieux, J. Giovannelli, and T. Rodet, Bayesian estimation of regularization and point spread function parameters for Wiener???Hunt deconvolution, Journal of the Optical Society of America A, vol.27, issue.7, pp.1593-1607, 2010.
DOI : 10.1364/JOSAA.27.001593

URL : https://hal.archives-ouvertes.fr/hal-00674508

]. M. Ozkan, A. M. Tekalp, and M. I. Sezan, POCS-based restoration of space-varying blurred images, IEEE Transactions on Image Processing, vol.3, issue.4, pp.450-454, 1994.
DOI : 10.1109/83.298398

]. P. Pankajakshan, Parametric Blind Deconvolution for Confocal Laser Scanning Microscopy, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.25-27, 2009.
DOI : 10.1109/IEMBS.2007.4353856

URL : https://hal.archives-ouvertes.fr/inria-00269265

]. P. Pankajakshan, B. Zhang, L. Blanc-féraud, Z. Kam, J. C. Olivo-marin et al., Blind deconvolution for thin-layered confocal imaging, Applied Optics, vol.48, issue.22, pp.4437-4448, 2009.
DOI : 10.1364/AO.48.004437

URL : https://hal.archives-ouvertes.fr/inria-00395523

]. P. Paul, H. Duessmann, T. Bernas, H. Huber, and D. Kalamatianos, Automatic noise quantification for confocal fluorescence microscopy images, Computerized Medical Imaging and Graphics, vol.34, issue.6, pp.426-434, 2010.
DOI : 10.1016/j.compmedimag.2010.04.001

]. J. Pawley, Handbook of biological confocal microscopy, 2006.

]. R. Paxman, T. J. Schulz, and J. R. Fienup, Joint estimation of object and aberrations by using phase diversity, Journal of the Optical Society of America A, vol.9, issue.7, pp.1072-1085, 1992.
DOI : 10.1364/JOSAA.9.001072

]. J. Pesquet, H. Krim, D. Leporini, and E. Hamman, Bayesian approach to best basis selection ICASSP- 96, Acoustics, Speech, and Signal Processing IEEE International Conference onCité en, pp.2634-2637, 1996.

]. 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

]. C. Preza and J. A. Conchello, Depth-variant maximum-likelihood restoration for three-dimensional fluorescence microscopy, Journal of the Optical Society of America A, vol.21, issue.9, pp.1593-1601, 2004.
DOI : 10.1364/JOSAA.21.001593

S. Ramani, C. Vonesch, and M. Unser, Deconvolution of 3D fluorescence micrographs with automatic risk minimization, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.732-735, 2008.
DOI : 10.1109/ISBI.2008.4541100

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

]. M. Re?ábek and P. Pata, The space variant PSF for deconvolution of widefield astronomical images, Proceedings of SPIE, p.70152, 2008.

]. B. Richards and E. Wolf, Electromagnetic Diffraction in Optical Systems. II. Structure of the Image Field in an Aplanatic System, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.253, issue.1274, pp.358-379, 1959.
DOI : 10.1098/rspa.1959.0200

]. W. 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. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, pp.259-268, 1992.
DOI : 10.1016/0167-2789(92)90242-F

]. L. Rudin and S. Osher, Total variation based image restoration with free local constraints, Proceedings of 1st International Conference on Image Processing, pp.31-35, 1994.
DOI : 10.1109/ICIP.1994.413269

]. T. Schulz, Multiframe blind deconvolution of astronomical images, Journal of the Optical Society of America A, vol.10, issue.5, pp.1064-1073, 1993.
DOI : 10.1364/JOSAA.10.001064

]. T. Schulz, B. Stribling, and J. Miller, Multiframe blind deconvolution with real data: imagery of the Hubble Space Telescope, Optics Express, vol.1, issue.11, pp.355-362, 1997.
DOI : 10.1364/OE.1.000355

H. Mi-sezan and . Stark, Image Restoration by the Method of Convex Projections: Part 2-Applications and Numerical Results, IEEE Transactions on Medical Imaging, vol.1, issue.2, pp.95-101, 1982.
DOI : 10.1109/TMI.1982.4307556

]. J. Shaevitz and D. A. Fletcher, Enhanced three-dimensional deconvolution microscopy using a measured depth-varying point-spread function, Journal of the Optical Society of America A, vol.24, issue.9, pp.2622-2627, 2007.
DOI : 10.1364/JOSAA.24.002622

]. A. Sharma, D. V. Kumar, and A. K. Ghatak, Tracing rays through graded-index media: a new method, Applied Optics, vol.21, issue.6, pp.984-987, 1982.
DOI : 10.1364/AO.21.000984

]. K. Sharma, Optics : principles and applications, 2006.

]. L. Shepp and Y. Vardi, Maximum Likelihood Reconstruction for Emission Tomography, IEEE Transactions on Medical Imaging, vol.1, issue.2, pp.113-122, 1982.
DOI : 10.1109/TMI.1982.4307558

]. B. Simon, Application des techniques de déconvolution et de tomograpohie diffractivè a l'amélioration de l'imagerie microscopique 2D et 3D, pp.151-153, 2007.

]. A. Stagliano, P. Boccacci, and M. Bertero, Analysis of an approximate model for Poisson data reconstruction and a related discrepancy principle, Inverse Problems, vol.27, issue.12, pp.125003-2011, 2011.
DOI : 10.1088/0266-5611/27/12/125003

T. G. Stockham-jr, T. M. Cannon, and R. B. Ingebretsen, Blind deconvolution through digital signal processing, Proceedings of the IEEE, pp.678-692, 1975.
DOI : 10.1109/PROC.1975.9800

]. P. Stokseth, Properties of a Defocused Optical System*, Journal of the Optical Society of America, vol.59, issue.10, pp.1314-1321, 1969.
DOI : 10.1364/JOSA.59.001314

]. P. Török, P. Varga, Z. Laczik, and . Booker, Electromagnetic diffraction of light focused through a planar interface between materials of mismatched refractive indices: an integral representation, Journal of the Optical Society of America A, vol.12, issue.2, pp.325-332, 1995.
DOI : 10.1364/JOSAA.12.000325

]. H. Trussell and B. Hunt, Image restoration of space variant blurs by sectioned methods, Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP'78, pp.196-198, 1978.

]. F. Tsumuraya, N. Miura, and N. Baba, Iterative blind deconvolution method using Lucy's algorithm, Astronomy and Astrophysics, vol.282, pp.699-708, 1994.

]. M. Unser, A. Aldroubi, and M. Eden, B-spline signal processing. I. Theory, IEEE Transactions on Signal Processing, vol.41, issue.2, pp.821-833, 1993.
DOI : 10.1109/78.193220

]. M. Unser, A. Aldroubi, and M. Eden, B-spline signal processing. II. Efficiency design and applications, IEEE Transactions on Signal Processing, vol.41, issue.2, pp.834-848, 1993.
DOI : 10.1109/78.193221

]. P. Van-laarhoven and E. H. Aarts, Simulated annealing : theory and applications, pp.33-52, 1987.
DOI : 10.1007/978-94-015-7744-1

]. C. Vogel, Computational methods for inverse problems, Society for Industrial Mathematics, vol.23, 2002.
DOI : 10.1137/1.9780898717570

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004.
DOI : 10.1109/TIP.2003.819861

]. Z. Xu and E. Y. Lam, Maximum a posteriori blind image deconvolution with Huber???Markov random-field regularization, Optics Letters, vol.34, issue.9, pp.1453-1455, 2009.
DOI : 10.1364/OL.34.001453

]. D. Youla and H. Webb, Image Restoration by the Method of Convex Projections: Part 1ߞTheory, IEEE Transactions on Medical Imaging, vol.1, issue.2, pp.81-94, 1982.
DOI : 10.1109/TMI.1982.4307555

]. S. Yuan and C. Preza, 3D fluorescence microscopy imaging accounting for depth-varying point-spread functions predicted by a strata interpolation method and a principal component analysis method, International society for optics and photonics (SPIE), pp.23298-23314, 2011.

]. R. Zanella, P. Boccacci, L. Zanni, and M. Bertero, Efficient gradient projection methods for edge-preserving removal of Poisson noise, Inverse Problems, vol.25, issue.52, pp.49-68, 2009.

]. B. Zhang, J. Zerubia, and J. C. Olivo-marin, Gaussian approximations of fluorescence microscope point-spread function models, Applied Optics, vol.46, issue.10, pp.1819-1829, 2007.
DOI : 10.1364/AO.46.001819

URL : https://hal.archives-ouvertes.fr/pasteur-00163734

]. X. Zhu and P. Milanfar, Automatic parameter selection for denoising algorithms using a no-reference measure of image content, Image Processing IEEE Transactions on, vol.19, issue.107, pp.3116-3132, 2010.

]. 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