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A primal-dual proximal splitting approach for restoring data corrupted with Poisson-Gaussian noise
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.
Cited literature [20 references]
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 : Wednesday, February 26, 2020 - 7:06:06 PM
Document(s) archivé(s) le : Tuesday, October 15, 2013 - 2:25:10 AM
Contributor : Emilie Chouzenoux <>
Submitted on : Sunday, July 14, 2013 - 6:36:55 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:06 PM
Document(s) archivé(s) le : Tuesday, October 15, 2013 - 2:25:10 AM
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Jezierska_2012_icassp.pdf
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