DELEDALLE, Charles-Alban; PAPADAKIS, Nicolas; SALMON, Joseph; VAITER, Samuel

Metadatos

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Licencia de uso del documento

DELEDALLE, Charles-Alban
Institut de Mathématiques de Bordeaux [IMB]

PAPADAKIS, Nicolas
Institut de Mathématiques de Bordeaux [IMB]

SALMON, Joseph
Institut Montpelliérain Alexander Grothendieck [IMAG]
Université de Montpellier [UM]

Idioma

Communication dans un congrès

Este ítem está publicado en

International Conference on Scale Space and Variational Methods in Computer Vision (SSVM'19), 2019-06-30, Hofgeismar. 2019 n° 11603, p. 131-143

Springer International Publishing

Resumen en inglés

In inverse problems, the use of an $\ell_{12}$ analysis regularizer induces a bias in the estimated solution. We propose a general refitting framework for removing this artifact while keeping information of interest contained in the biased solution. This is done through the use of refitting block penalties that only act on the co-support of the estimation. Based on an analysis of related works in the literature, we propose a new penalty that is well suited for refitting purposes. We also present an efficient algorithmic method to obtain the refitted solution along with the original (biased) solution for any convex refitting block penalty. Experiments illustrate the good behavior of the proposed block penalty for refitting.< Leer menos

URI

https://oskar-bordeaux.fr/handle/20.500.12278/192810

Proyecto europeo

Nonlocal Methods for Arbitrary Data Sources

Proyecto ANR

Generalized Optimal Transport Models for Image processing - ANR-16-CE33-0010

Orígen

Importado de Hal

Centros de investigación

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251