A primal-dual proximal splitting approach for restoring data corrupted with Poisson-Gaussian noise - Archive ouverte HAL Access content directly
Conference Papers Year : 2012

A primal-dual proximal splitting approach for restoring data corrupted with Poisson-Gaussian noise

(1) , (1) , (1) , (1)
1

Abstract

A Poisson-Gaussian model accurately describes the noise present in many imaging systems such as CCD cameras or fluorescence microscopy. However most existing restoration strategies rely on approximations of the Poisson-Gaussian noise statistics. We propose a convex optimization algorithm for the reconstruction of signals degraded by a linear operator and corrupted with mixed Poisson-Gaussian noise. The originality of our approach consists of considering the exact continuous-discrete model corresponding to the data statistics. After establishing the Lipschitz differentiability of the Poisson-Gaussian log-likelihood, we derive a primal-dual iterative scheme for minimizing the associated penalized criterion. The proposed method is applicable to a large choice of penalty terms. The robustness of our scheme allows us to handle computational difficulties due to infinite sums arising from the computation of the gradient of the criterion. The proposed approach is validated on image restoration examples.
Fichier principal
Vignette du fichier
Jezierska_2012_icassp.pdf (241.33 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Loading...

Dates and versions

hal-00687634 , version 1 (14-07-2013)

Identifiers

Cite

Anna Jezierska, Emilie Chouzenoux, Jean-Christophe Pesquet, Hugues Talbot. A primal-dual proximal splitting approach for restoring data corrupted with Poisson-Gaussian noise. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), Mar 2012, Kyoto, Japan. pp.1085 - 1088, ⟨10.1109/ICASSP.2012.6288075⟩. ⟨hal-00687634⟩
169 View
483 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More