Title :

Non-linear aggregation of filters to improve image denoising

Author(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]

Conference title :

Computing Conference 2020

City :

London

Country :

Royaume-Uni

Start date of the conference :

2020-07-16

English keyword(s) :

Ensemble methods
Image denoising
Statistical aggregation
Collaborative filtering

HAL domain(s) :

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

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL