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

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

M. Bertero, P. Boccacci, G. Talenti, R. Zanella, and L. Zanni, A discrepancy principle for Poisson data, Inverse Problems, vol.26, issue.10, 2010.
DOI : 10.1088/0266-5611/26/10/105004

A. Grinvald and I. Z. Steinberg, On the analysis of fluorescence decay kinetics by the method of least-squares, Analytical Biochemistry, vol.59, issue.2, pp.583-598, 1974.
DOI : 10.1016/0003-2697(74)90312-1

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

R. Glowinski, Numerical Methods for Nonlinear variation al Problems, 1984.

P. Weiss, L. Blanc-féraud, and G. Aubert, Efficient Schemes for Total Variation Minimization Under Constraints in Image Processing, SIAM Journal on Scientific Computing, vol.31, issue.3, pp.2047-2080, 2009.
DOI : 10.1137/070696143
URL : https://hal.archives-ouvertes.fr/inria-00166096

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