Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadata

Cited literature [19 references]  Display  Hide  Download

https://hal.inria.fr/hal-02086856
Contributor : Benjamin Guedj <>
Submitted on : Tuesday, October 1, 2019 - 8:57:09 PM
Last modification on : Friday, February 5, 2021 - 3:29:24 AM

File

main.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02086856, version 2

Citation

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

Share

Metrics

Record views

120

Files downloads

580