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

Anna Jezierska 1 Emilie Chouzenoux 1 Jean-Christophe Pesquet 1 Hugues Talbot 1
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
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.
keyword : deconvolution convex optimization image restoration denoising deconvolution.
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), Mar 2012, Kyoto, Japan. pp.1085 - 1088, 2012, 〈10.1109/ICASSP.2012.6288075〉
Liste complète des métadonnées

Littérature citée [20 références]  Voir  Masquer  Télécharger
https://hal-upec-upem.archives-ouvertes.fr/hal-00687634
Contributeur : Emilie Chouzenoux <>
Soumis le : dimanche 14 juillet 2013 - 18:36:55
Dernière modification le : jeudi 5 juillet 2018 - 14:29:12
Document(s) archivé(s) le : mardi 15 octobre 2013 - 02:25:10

Fichier

Jezierska_2012_icassp.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

Collections

LIGM_A3SI | UNIV-MLV | LIGM_SIGNAL | LIGM | PARISTECH | ENPC | UPEC-UPEM | ESIEE-PARIS

Citation

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, 2012, 〈10.1109/ICASSP.2012.6288075〉. 〈hal-00687634〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

362

Téléchargements de fichiers

341

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD