Simultaneous deconvolution and denoising using a second order variational approach applied to image super resolution

Abstract : The aim of a Super Resolution (SR) technique is to construct a high-resolution image from a sequence of observed low-resolution ones of the same scene. One major roadblock of an SR reconstitution is removing noise and blur without destroying edges. We propose a novel multiframe image SR algorithm based on a convex combination of Bilateral Total Variation and a non-smooth second order variational regulariza-tion, using a controlled weighting parameter. We prove the existence of a minimizer of the proposed SR model in the space of functions of bounded Hessian, and we confirm the success of this approach in avoiding undesirable artifacts. The simulation results show the efficient performance of the proposed algorithm compared to other methods in the literature using two criteria, PSNR and SSIM.
Type de document :
Article dans une revue
Computer Vision and Image Understanding, Elsevier, 2017, 〈10.1016/j.cviu.2017.08.007〉
Liste complète des métadonnées

Littérature citée [26 références]  Voir  Masquer  Télécharger

https://hal-enpc.archives-ouvertes.fr/hal-01616743
Contributeur : Pascal Monasse <>
Soumis le : samedi 14 octobre 2017 - 09:48:31
Dernière modification le : mercredi 11 avril 2018 - 12:12:03
Document(s) archivé(s) le : lundi 15 janvier 2018 - 12:32:33

Fichier

manuscript3.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Amine Laghrib, Mahmoud Ezzaki, Mohammed El Rhabi, Abdelilah Hakim, Pascal Monasse, et al.. Simultaneous deconvolution and denoising using a second order variational approach applied to image super resolution. Computer Vision and Image Understanding, Elsevier, 2017, 〈10.1016/j.cviu.2017.08.007〉. 〈hal-01616743〉

Partager

Métriques

Consultations de la notice

123

Téléchargements de fichiers

148