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

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.
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Submitted on : Wednesday, July 3, 2013 - 4:33:11 PM
Last modification on : Saturday, January 15, 2022 - 3:58:18 AM
Long-term archiving on: : Friday, October 4, 2013 - 2:20:09 AM

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  • HAL Id : hal-00733786, version 1

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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. ⟨hal-00733786⟩

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