CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration

Abstract : In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus on image processing tasks. Generalizing ideas that emerged for l(1) regularization, we develop an approach refitting the results of standard methods toward the input data. Total variation regularizations and nonlocal means are special cases of interest. We identify important covariant information that should be preserved by the refitting method and emphasize the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we provide an approach that has a “twicing” flavor and allows refitting the restored signal by adding back a local affine transformation of the residual term. We illustrate the benefits of our method on numerical simulations for image restoration tasks. Read More: http://epubs.siam.org/doi/abs/10.1137/16M1080318
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Article dans une revue
SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2017, 10 (1), pp.243 - 284. <10.1137/16M1080318>
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Contributeur : Nicolas Papadakis <>
Soumis le : mercredi 7 juin 2017 - 13:25:09
Dernière modification le : samedi 8 juillet 2017 - 01:01:54

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Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter. CLEAR: Covariant LEAst-Square Refitting with Applications to Image Restoration. SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2017, 10 (1), pp.243 - 284. <10.1137/16M1080318>. <hal-01534202>

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