Non-linear aggregation of filters to improve ...
Type de document :
Communication dans un congrès avec actes
URL permanente :
Titre :
Non-linear aggregation of filters to improve image denoising
Auteur(s) :
Titre de la manifestation scientifique :
Computing Conference 2020
Ville :
London
Pays :
Royaume-Uni
Date de début de la manifestation scientifique :
2020-07-16
Date de publication :
2020-07-16
Mot(s)-clé(s) :
Statistical aggregation
Ensemble methods
Image denoising
Collaborative filtering
Ensemble methods
Image denoising
Collaborative filtering
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [en]
We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity based on how the pool of filters reaches a ...
Lire la suite >We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity based on how the pool of filters reaches a consensus. We provide a theoretical bound to support our aggregation scheme, its numerical performance is illustrated and we show that the aggregate significantly outperforms each of the preliminary filters.Lire moins >
Lire la suite >We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity based on how the pool of filters reaches a consensus. We provide a theoretical bound to support our aggregation scheme, its numerical performance is illustrated and we show that the aggregate significantly outperforms each of the preliminary filters.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CNRS
Université de Lille
Université de Lille
Date de dépôt :
2020-06-08T14:10:20Z