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Pré-Publication, Document De Travail Année : 2016

CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration

Résumé

In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for l1 regularization, we develop an approach re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting 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 re-fitting 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.
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Dates et versions

hal-01333295 , version 1 (17-06-2016)
hal-01333295 , version 2 (14-09-2016)
hal-01333295 , version 3 (06-12-2016)

Identifiants

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Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter. CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration. 2016. ⟨hal-01333295v2⟩
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