General Adaptive Neighborhood Choquet Image Filtering

Abstract : A novel framework entitled General Adaptive Neighborhood Image Processing (GANIP) has been recently introduced in order to propose an original image representation and mathematical structure for adaptive image processing and analysis. The central idea is based on the key notion of adaptivity which is simultaneously associated with the analyzing scales, the spatial structures and the intensity values of the image to be addressed. In this paper, the GANIP framework is briefly exposed and particularly studied in the context of Choquet filtering (using fuzzy measures), which generalizes a large class of image filters. The resulting spatially-adaptive operators are studied with respect to the general GANIP framework and illustrated in both the biomedical and materials application areas. In addition, the proposed GAN-based filters are practically applied and compared to several other denoising methods through experiments on image restoration, showing a high performance of the GAN-based Choquet filters.
Liste complète des métadonnées

https://hal-emse.ccsd.cnrs.fr/emse-00445831
Contributeur : Andrée-Aimée Toucas <>
Soumis le : lundi 11 janvier 2010 - 14:24:16
Dernière modification le : mardi 23 octobre 2018 - 14:36:09

Lien texte intégral

Identifiants

Citation

Johan Debayle, Jean-Charles Pinoli. General Adaptive Neighborhood Choquet Image Filtering. Journal of Mathematical Imaging and Vision, Springer Verlag, 2009, 35 (3), pp.173-175. 〈10.1007/s10851-009-0163-0〉. 〈emse-00445831〉

Partager

Métriques

Consultations de la notice

77