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Non-linear Aggregation of Filters to Improve Image Denoising.

Guedj, B; Rengot, J; (2020) Non-linear Aggregation of Filters to Improve Image Denoising. In: Arai, K and Kapoor, S and Bhatia, R, (eds.) SAI 2020: Intelligent Computing. (pp. pp. 314-327). Springer: London, UK. Green open access

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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.

Type: Proceedings paper
Title: Non-linear Aggregation of Filters to Improve Image Denoising.
Event: Science and Information Conference
ISBN-13: 978-3-030-52245-2
Open access status: An open access version is available from UCL Discovery
Publisher version: https://doi.org/10.1007/978-3-030-52246-9_22
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: image denoising, statistical aggregation, ensemble methods, collaborative filtering
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10107086
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