Patch similarity under non Gaussian noise

Abstract : Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. A fundamental difficulty when comparing two patches from "real" data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared intensity differences. When the noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature. We review seven of those criteria taken from the fields of image processing, detection theory and machine learning. We discuss their theoretical grounding and provide a numerical comparison of their performance under Gamma and Poisson noises.
Type de document :
Communication dans un congrès
International Conference on Image Processing, Sep 2011, Brussels, Belgium. pp.1845 - 1848, 2011, 〈10.1109/ICIP.2011.6115825〉
Liste complète des métadonnées

Littérature citée [14 références]  Voir  Masquer  Télécharger

https://hal-ujm.archives-ouvertes.fr/ujm-00985629
Contributeur : Loïc Denis <>
Soumis le : mercredi 30 avril 2014 - 10:44:05
Dernière modification le : mercredi 25 juillet 2018 - 14:05:31
Document(s) archivé(s) le : mercredi 30 juillet 2014 - 12:05:10

Fichier

ICIP_2011a.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Charles-Alban Deledalle, Florence Tupin, Loïc Denis. Patch similarity under non Gaussian noise. International Conference on Image Processing, Sep 2011, Brussels, Belgium. pp.1845 - 1848, 2011, 〈10.1109/ICIP.2011.6115825〉. 〈ujm-00985629〉

Partager

Métriques

Consultations de la notice

245

Téléchargements de fichiers

126