Joint coding-denoising optimization of noisy images

Mikael Carlavan 1 Laure Blanc-Féraud 1 Marc Antonini 2 Carole Thiebaut 3 Christophe Latry 3 Yves Bobichon 4
1 MORPHEME - Morphologie et Images
CRISAM - Inria Sophia Antipolis - Méditerranée , IBV - Institut de Biologie Valrose : U1091, Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : In this paper, we propose to study the problem of noisy source coding/denoising. The challenge of this problem is that a global optimization is usually difficult to perform as the global fidelity criterion needs to be optimized in the same time over the sets of both coding and denoising parameters. Most of the bibliography in this domain is based on the fact that, for a specific criterion, the global optimization problem can be simply separated into two independent optimization problems: The noisy image should be first optimally denoised and this denoised image should then be optimally coded. In many applications however, the layout of the acquisition imaging chain is fixed and cannot be changed, that is a denoising step cannot be inserted before coding. For this reason, we are concerned here with the problem of global joint optimization in the case the denoising step is performed, as usual, after coding/decoding. In this configuration, we show how to express the global distortion as a function of the coding and denoising parameters. We present then an algorithm to minimize this distortion and to get the optimal values of these parameters. We show results of this joint optimization algorithm on classical test images and on a high dynamic range image, visually and in a rate-distortion sense.
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Mikael Carlavan, Laure Blanc-Féraud, Marc Antonini, Carole Thiebaut, Christophe Latry, et al.. Joint coding-denoising optimization of noisy images. [Research Report] I3S. 2013. ⟨hal-00773604⟩

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