Non-linear aggregation of filters to improve ...
Document type :
Communication dans un congrès avec actes
Permalink :
Title :
Non-linear aggregation of filters to improve image denoising
Author(s) :
Conference title :
Computing Conference 2020
City :
London
Country :
Royaume-Uni
Start date of the conference :
2020-07-16
Publication date :
2020-07-16
Keyword(s) :
Statistical aggregation
Ensemble methods
Image denoising
Collaborative filtering
Ensemble methods
Image denoising
Collaborative filtering
HAL domain(s) :
Statistiques [stat]/Machine Learning [stat.ML]
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 >
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
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CNRS
Université de Lille
Université de Lille
Submission date :
2020-06-08T14:10:20Z
2020-06-09T08:27:42Z
2020-06-09T08:27:42Z
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