Adaptive parameter selection for gradient-sparse + low patch-rank recovery: application to image decomposition
Idioma
en
Document de travail - Pré-publication
Resumen en inglés
In this work, we are interested in gradient sparse + low patchrank signal recovery for image structure-texture decomposition. We locally model the structure as gradient-sparse and the texture as of low patch-rank. Moreover, ...Leer más >
In this work, we are interested in gradient sparse + low patchrank signal recovery for image structure-texture decomposition. We locally model the structure as gradient-sparse and the texture as of low patch-rank. Moreover, we propose a rule based upon theoretical results of sparse + low-rank matrix recovery in order to automatically tune our model depending on the local content and we numerically validate this proposition.< Leer menos
Palabras clave en inglés
Image decomposition
texture
optimization
gradient-sparsity
low patch-rank
Proyecto ANR
Régularisation performante de problèmes inverses en grande dimension pour le traitement de données - ANR-20-CE40-0001
Orígen
Importado de HalCentros de investigación