A convex approach to superresolution and regularization of lines in images - Calcul des Variations, Géométrie, Image Accéder directement au contenu
Article Dans Une Revue SIAM Journal on Imaging Sciences Année : 2019

A convex approach to superresolution and regularization of lines in images

Résumé

We present a new convex formulation for the problem of recovering lines in degraded images. Following the recent paradigm of super-resolution, we formulate a dedicated atomic norm penalty and we solve this optimization problem by means of a primal-dual algorithm. This parsimonious model enables the reconstruction of lines from lowpass measurements, even in presence of a large amount of noise or blur. Furthermore, a Prony method performed on rows and columns of the restored image, provides a spectral estimation of the line parameters, with subpixel accuracy.
Fichier principal
Vignette du fichier
Polisano2018Convex.pdf (1.98 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01599010 , version 1 (30-09-2017)
hal-01599010 , version 2 (27-04-2018)
hal-01599010 , version 3 (10-10-2018)
hal-01599010 , version 4 (19-11-2018)

Identifiants

Citer

Kévin Polisano, Laurent Condat, Marianne Clausel, Valérie Perrier. A convex approach to superresolution and regularization of lines in images. SIAM Journal on Imaging Sciences, 2019, 12 (1), pp.211-258. ⟨10.1137/18M118116X⟩. ⟨hal-01599010v4⟩
576 Consultations
690 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More