A nonparametric minimum entropy image deblurring algorithm

Abstract : In this paper we address the image restoration problem in the variational framework. Classical approaches minimize the Lp norm of the residual and rely on parametric assumptions on the noise statistical model. We relax this parametric hypothesis and we formulate the problem on the basis of nonparametric density estimates. The proposed approach minimizes the residual differential entropy. Experimental results with non gaussian distributions show the interest of such a nonparametric approach. Images quality is evaluated by means of the PSNR measure and SSIM index, more adapted to the human visual system.
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech, and Signal Processing, Mar 2008, Las Vegas, Nevada, United States. 2008
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00379328
Contributeur : Cesario Vincenzo Angelino <>
Soumis le : mardi 28 avril 2009 - 12:00:43
Dernière modification le : jeudi 14 mai 2009 - 11:18:34
Document(s) archivé(s) le : lundi 15 octobre 2012 - 09:35:24

Fichier

ICASSP08.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00379328, version 1

Collections

Citation

Cesario Vincenzo Angelino, Eric Debreuve, Michel Barlaud. A nonparametric minimum entropy image deblurring algorithm. IEEE International Conference on Acoustics, Speech, and Signal Processing, Mar 2008, Las Vegas, Nevada, United States. 2008. <hal-00379328>

Partager

Métriques

Consultations de
la notice

148

Téléchargements du document

54