Titre :

Non-linear aggregation of filters to improve image denoising

Auteur(s) :

Guedj, Benjamin [Auteur]
The Inria London Programme [Inria-London]
Inria-CWI [Inria-CWI]
University College of London [London] [UCL]
MOdel for Data Analysis and Learning [MODAL]
Department of Computer science [University College of London] [UCL-CS]
Rengot, Juliette [Auteur]
École nationale des ponts et chaussées [ENPC]

Titre de la manifestation scientifique :

Computing Conference 2020

Ville :

London

Pays :

Royaume-Uni

Date de début de la manifestation scientifique :

2020-07-16

Mot(s)-clé(s) en anglais :

Ensemble methods
Image denoising
Statistical aggregation
Collaborative filtering

Discipline(s) HAL :

Statistiques [stat]/Machine Learning [stat.ML]
Statistiques [stat]/Méthodologie [stat.ME]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Traitement des images [eess.IV]

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 >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL