Learning to Estimate and Remove Non-uniform Image Blur

Florent Couzinie-Devy 1, 2 Jian Sun 1, 2, 3 Karteek Alahari 1, 2 Jean Ponce 1, 2
1 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
2 LIENS - Laboratoire d'informatique de l'école normale supérieure
3 Xjtu - Xi'an Jiaotong University
Abstract : This paper addresses the problem of restoring images subjected to unknown and spatially varying blur caused by defocus or linear (say, horizontal) motion. The estimation of the global (non-uniform) image blur is cast as a multi-label energy minimization problem. The energy is the sum of unary terms corresponding to learned local blur estimators, and binary ones corresponding to blur smoothness. Its global minimum is found using Ishikawa's method by exploiting the natural order of discretized blur values for linear motions and defocus. Once the blur has been estimated, the image is restored using a robust (non-uniform) deblurring algorithm based on sparse regularization with global image statistics. The proposed algorithm outputs both a segmentation of the image into uniform-blur layers and an estimate of the corresponding sharp image. We present qualitative results on real images, and use synthetic data to quantitatively compare our approach to the publicly available implementation of Chakrabarti et al. (2010).
Type de document :
Communication dans un congrès
CVPR - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2013, Portland, United States. IEEE, pp.1075-1082, 2013, 〈10.1109/CVPR.2013.143〉
Liste complète des métadonnées

Littérature citée [36 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/hal-00818175
Contributeur : Karteek Alahari <>
Soumis le : jeudi 17 octobre 2013 - 18:57:11
Dernière modification le : mardi 17 avril 2018 - 11:30:05
Document(s) archivé(s) le : samedi 18 janvier 2014 - 02:50:19

Fichier

couzinie-devy13.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA | PSL

Citation

Florent Couzinie-Devy, Jian Sun, Karteek Alahari, Jean Ponce. Learning to Estimate and Remove Non-uniform Image Blur. CVPR - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2013, Portland, United States. IEEE, pp.1075-1082, 2013, 〈10.1109/CVPR.2013.143〉. 〈hal-00818175〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

453

Téléchargements de fichiers

230

Contact


archive-ouverte@inria.fr