Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Journal of Mathematical Imaging and Vision Année : 2022

Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems

Résumé

We introduce an algorithm to solve linear inverse problems regularized with the total (gradient) variation in a gridless manner. Contrary to most existing methods, that produce an approximate solution which is piecewise constant on a fixed mesh, our approach exploits the structure of the solutions and consists in iteratively constructing a linear combination of indicator functions of simple polygons.
Fichier principal
Vignette du fichier
main.pdf (2.33 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03406710 , version 1 (09-11-2021)
hal-03406710 , version 2 (11-04-2022)
hal-03406710 , version 3 (09-07-2022)

Identifiants

Citer

Yohann de Castro, Vincent Duval, Romain Petit. Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems. Journal of Mathematical Imaging and Vision, 2022, ⟨10.1007/s10851-022-01115-w⟩. ⟨hal-03406710v3⟩

Relations

225 Consultations
144 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More