A penalized weighted least squares approach for restoring data corrupted with signal-dependent noise

Audrey Repetti 1 Emilie Chouzenoux 1 Jean-Christophe Pesquet 1
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
Abstract : This paper addresses the problem of recovering an image degraded by a linear operator and corrupted with an additive Gaussian noise with a signal-dependent variance. The considered observation model arises in several digital imaging devices. To solve this problem, a variational approach is adopted relying on a weighted least squares criterion which is penalized by a non-smooth function. In this context, the choice of an efficient optimization algorithm remains a challenging task. We propose here to extend a recent primal-dual proximal splitting approach by introducing a preconditioning strategy that is shown to significantly speed up the algorithm convergence. The good performance of the proposed method is illustrated through image restoration examples.
keyword : preconditioning convex optimization image restoration denoising deconvolution total variation regularization
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Communication dans un congrès
20th European Signal Processing Conference (EUSIPCO 2012), Aug 2012, Bucarest, Romania. pp.1553-1557, 2012
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Audrey Repetti, Emilie Chouzenoux, Jean-Christophe Pesquet. A penalized weighted least squares approach for restoring data corrupted with signal-dependent noise. 20th European Signal Processing Conference (EUSIPCO 2012), Aug 2012, Bucarest, Romania. pp.1553-1557, 2012. 〈hal-00733786〉

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