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

D. A. Agard, Optical Sectioning Microscopy: Cellular Architecture in Three Dimensions, Annual Review of Biophysics and Bioengineering, vol.13, issue.1, pp.191-219, 1984.
DOI : 10.1146/annurev.bb.13.060184.001203

R. Hudson, J. N. Aarsvold, C. Chen, J. Chen, P. Davies et al., Optical microscopy system for 3D dynamic imaging, Proc. SPIE, pp.187-198, 1996.

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

J. G. Mcnally, C. Preza, J. Conchello, and L. J. Thomas-jr, Artifacts in computational optical-sectioning microscopy, Journal of the Optical Society of America A, vol.11, issue.3, pp.1056-1067, 1994.
DOI : 10.1364/JOSAA.11.001056

J. W. 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
DOI : 10.1364/JOSAA.24.002622

P. J. Shaw and D. J. Rawlins, The point-spread function of a confocal microscope: its measurement and use in deconvolution of 3-D data, Journal of Microscopy, vol.30, issue.7, pp.151-165, 1991.
DOI : 10.1111/j.1365-2818.1991.tb03168.x

P. J. Shaw, Deconvolution in 3-D optical microscopy, The Histochemical Journal, vol.30, issue.9, pp.687-694, 1994.
DOI : 10.1007/BF00158201

T. J. Holmes, Maximum-likelihood image restoration adapted for noncoherent optical imaging, Journal of the Optical Society of America A, vol.5, issue.5, pp.666-673, 1988.
DOI : 10.1364/JOSAA.5.000666

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

P. Pankajakshan, B. Zhang, L. Blanc-féraud, Z. Kam, J. C. Olivo-marin et al., Parametric Blind Deconvolution for Confocal Laser Scanning Microscopy (CLSM)-Proof of Concept, Research Report, vol.6493, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00269265

G. M. Van-kempen, L. J. Van-vliet, P. J. Verveer, H. T. Van-der, and . Voort, A quantitative comparison of image restoration methods for confocal microscopy, Journal of Microscopy, vol.185, issue.3, pp.354-365, 1997.
DOI : 10.1046/j.1365-2818.1997.d01-629.x

A. Dieterlen, C. Xu, O. Haeberle, N. Hueber, R. Malfara et al., Identification and restoration in 3D fluorescence microscopy, Proc. SPIE, pp.105-113, 2004.

G. Demoment, Image reconstruction and restoration: overview of common estimation structures and problems, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.37, issue.12, pp.2024-2036, 1989.
DOI : 10.1109/29.45551

A. N. Tikhonov and V. A. Arsenin, Solution of Ill-posed Problems, 1977.

K. Miller, Least Squares Methods for Ill-Posed Problems with a Prescribed Bound, SIAM Journal on Mathematical Analysis, vol.1, issue.1, pp.52-74, 1970.
DOI : 10.1137/0501006

L. I. 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

N. Dey, L. Blanc-féraud, C. Zimmer, P. Roux, Z. Kam et al., 3D Microscopy Deconvolution using Richardson-Lucy Algorithm with Total Variation Regularization, Research Report, vol.5272, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00070726

L. B. Lucy, An iterative technique for the rectification of observed distributions, The Astronomical Journal, vol.79
DOI : 10.1086/111605

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

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society B, vol.39, issue.1, pp.1-38, 1977.

M. Jiang and G. Wang, Development of blind image deconvolution and its applications, Journal of X-Ray Science and Technology, vol.11, pp.13-19, 2003.

T. F. Chan and C. Wong, Total variation blind deconvolution, IEEE Transactions on Image Processing, vol.7, issue.3, pp.370-375, 1998.
DOI : 10.1109/83.661187

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.1221

L. Bar, N. A. Sochen, and N. Kiryati, Variational Pairing of Image Segmentation and Blind Restoration, Proc. ECCV, volume II, pp.166-177, 2004.
DOI : 10.1007/978-3-540-24671-8_13

A. Santos and I. T. Young, Model-based resolution: applying the theory in quantitative microscopy, Applied Optics, vol.39, issue.17, pp.2948-2958, 2000.
DOI : 10.1364/AO.39.002948

K. E. Atkinson, An introduction to Numerical Analysis, 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. Mohammad-djafari, A Full Bayesian Approach for Inverse Problems, Maximum entropy and Bayesian methods, pp.135-143, 1996.
DOI : 10.1007/978-94-011-5430-7_16

URL : http://arxiv.org/abs/physics/0111123

P. Pankajakshan, B. Zhang, L. Blanc-féraud, Z. Kam, J. C. Olivo-marin et al., Parametric Blind Deconvolution for Confocal Laser Scanning Microscopy, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.6531-6534, 2007.
DOI : 10.1109/IEMBS.2007.4353856

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

M. De-moraes-marim, B. Zhang, J. Olivo-marin, and C. Zimmer, Improving single particle localization with an empirically calibrated Gaussian kernel, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1003-1006, 2008.
DOI : 10.1109/ISBI.2008.4541168

P. Pankajakshan, B. Zhang, L. Blanc-féraud, Z. Kam, J. C. Olivo-marin et al., Blind deconvolution for diffraction-limited fluorescence microscopy, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.740-743, 2008.
DOI : 10.1109/ISBI.2008.4541102

D. Signaling, &. Biology, U. Cancer, . Cnrs, and . Unsa, and (b) restored image ( c Ariana-INRIA/I3S). The intensity is scaled between

. Fig, Observed root apex of an Arabidopsis Thaliana with a volume 146 The sub-volume chosen for restoration is emphasized

. Fig, Rendered sub-volume of the (a) observed image slices in Fig. 7 ( c INRA) and (b) volume rendering of the restored image slices