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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [20 references]  Display  Hide  Download
https://hal-upec-upem.archives-ouvertes.fr/hal-00687634
Contributor : Emilie Chouzenoux <>
Submitted on : Sunday, July 14, 2013 - 6:36:55 PM
Last modification on : Thursday, July 5, 2018 - 2:29:12 PM
Long-term archiving on : Tuesday, October 15, 2013 - 2:25:10 AM

File

Jezierska_2012_icassp.pdf
Publisher files allowed on an open archive

Identifiers

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, ⟨10.1109/ICASSP.2012.6288075⟩. ⟨hal-00687634⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

411

Files downloads

497

Contact

CCSD