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

doi:10.1007/978-3-030-22368-7_11

Metadatos

Licencia de uso del documento

DELEDALLE, Charles-Alban
Department of Electrical and Computer Engineering [Univ California San Diego] [ECE - UC San Diego]
Institut de Mathématiques de Bordeaux [IMB]

PAPADAKIS, Nicolas
Centre National de la Recherche Scientifique [CNRS]
Institut de Mathématiques de Bordeaux [IMB]

SALMON, Joseph
Institut Montpelliérain Alexander Grothendieck [IMAG]

Idioma

Chapitre d'ouvrage

Este ítem está publicado en

Scale Space and Variational Methods in Computer Vision, Scale Space and Variational Methods in Computer Vision. 2019-06-05, vol. 11603, p. 131-143

Resumen en inglés

In inverse problems, the use of an l(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

Palabras clave en inglés

Total variation

Bias correction

Refitting

URI

https://oskar-bordeaux.fr/handle/20.500.12278/191677

DOI

http://dx.doi.org/10.1007/978-3-030-22368-7_11

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