E. D. Kolaczyk, Bayesian multi-scale models for poisson processes, J. Amer. Stat. Assoc, vol.94, pp.920-933, 1999.

S. Lefkimmiatis, M. , P. Papandreou, and G. , Bayesian inference on multiscale models for poisson intensity estimation: Applications to photon-limited image denoising, IEEE Trans. Image Process, vol.18, pp.1724-1741, 2009.

S. A. Haider, Fluorescence microscopy image noise reduction using a stochastically-connected random field model, Scientific Reports, vol.6, p.20640, 2016.

A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, A versatile wavelet domain noise filtration technique for medical imaging, IEEE Trans. Med. Imag, vol.22, pp.323-331, 2003.

P. Fryzlewicz and G. P. Nason, A Haar-Fisz algorithm for poisson intensity estimation, J. Comput. Graph. Stat, vol.13, pp.621-638, 2004.

T. Blu and F. Luisier, The sure-let approach to image denoising, IEEE Trans. Image Process, vol.16, pp.2778-2786, 2007.

F. Luisier, T. Blu, and M. Unser, Image denoising in mixed poisson-gaussian noise, IEEE Trans. Image Process, vol.20, pp.696-708, 2011.

A. Baudes, A non-local algorithm for image denoising, Computer Vision and Pattern Recognition, vol.2, pp.60-65, 2005.

J. Boulanger, Patch-based non-local functional for denoising fluorescence microscopy image sequences, IEEE Trans. on Medical Imaging, vol.29, 2010.

K. Dabov, V. Foi, A. Egiazarian, and K. , Image denoising by sparse 3d transform-domain collaborative filtering, IEEE Trans. Image Process, vol.16, pp.2080-2095, 2007.

Y. L. Montagner, E. D. Angelini, and J. C. Olivo-marin, An unbiased risk estimator for image denoising in the presence of mixed poisson-gaussian noise, IEEE Trans. Image Process, vol.23, pp.1255-68, 2014.

C. M. Stein, Estimation of the mean of a multivariate normal distribution, Ann. Statist, vol.9, pp.1135-1151, 1981.

M. Makitalo and A. Foi, Optimal inversion of the generalized anscombe transformation for poisson-gaussian noise, IEEE Trans. Image Process, vol.22, pp.91-103, 2013.

L. Azzari and A. Foi, Variance stabilization for noisy+estimate combination in iterative poisson denoising, IEEE Signal Process. Lett, vol.23, pp.1086-1090, 2016.

F. J. Anscombe, The transformation of poisson, binomial and negative-binomial data, Biometrika, vol.35, pp.246-254, 1948.

M. Elad and M. Aharon, Image denoising via sparse and redundant representations over learned dictionaries, IEEE Trans. Image Process, vol.15, pp.3736-3745, 2006.

A. Arenodo, E. Bacry, and J. Muzy, The thermodynamics of fractals revisited with wavelets, Physica A, vol.213, pp.232-275, 1995.

K. Falconer, Techniques in fractal geometry, 1997.

A. Turiel, H. Yahia, and C. J. &pérez-vicente, Microcanonical multifractal formalism -a geometrical approach to multifractal systems: Part 1. Singularity analysis, J. Phys. A: Math. Theor, p.41, 2008.

S. K. Maji, Multiscale methods in signal processing for adaptive optics, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00909085

A. Turiel and A. Pozo, Reconstructing images from their most singular fractal manifold, IEEE Trans. Image Process, vol.11, pp.345-350, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00532760

A. Turiel and N. Parga, The multifractal structure of contrast changes in natural images: From sharp edges to textures, Neural Computation, vol.12, pp.763-793, 2000.

J. Weickert, Anisotropic diffusion in image processing, 2006.

A. Lehmussola, P. Ruusuvuori, J. Selinummi, H. Huttunen, and O. Yli-harja, Computational framework for simulating fluorescence microscope images with cell populations, IEEE Trans. Med. Imaging, vol.26, pp.1010-1016, 2007.

M. Riffle and T. N. Davis, The yeast resource center public image repository: A large database of fluorescence microscopy images, BMC Bioinformatics, vol.11, p.263, 2010.