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Non-linear aggregation of filters to improve image denoising

Benjamin Guedj 1, 2, 3, 4, 5 Juliette Rengot 6 
2 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
Abstract : 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.
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Submitted on : Tuesday, October 1, 2019 - 8:57:09 PM
Last modification on : Thursday, March 24, 2022 - 3:12:58 AM


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  • HAL Id : hal-02086856, version 2


Benjamin Guedj, Juliette Rengot. Non-linear aggregation of filters to improve image denoising. Computing Conference 2020, Jul 2020, London, United Kingdom. ⟨hal-02086856v2⟩



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