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A review of an old dilemma: Demosaicking first, or denoising first?

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

Image denoising and demosaicking are the first two crucial steps in digital camera pipelines. In most of the literature, denoising and demosaicking are treated as two independent problems, without considering their interaction, or asking which should be applied first. Several recent works have started addressing them jointly in works that involve heavy weight neural networks, thus incompatible with low power portable imaging devices. Hence, the question of how to combine denoising and demosaicking to reconstruct full color images remains very relevant: Is denoising to be applied first, or should that be demosaicking first? In this paper, we review the main variants of these strategies and carry-out an extensive evaluation to find the best way to reconstruct full color images from a noisy mosaic. We conclude that demosaicking should applied first, followed by denoising. Yet we prove that this requires an adaptation of classic denoising algorithms to demosaicked noise, which we justify and specify. © 2020 IEEE.
Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
PublisherIEEE
Pages2169-2179
ISBN (Electronic)9781728193601
ISBN (Print)978-1-7281-9361-8
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2020) - Virtual, Seattle, United States
Duration: 14 Jun 202019 Jun 2020

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2020)
PlaceUnited States
CitySeattle
Period14/06/2019/06/20

Funding

Work partly financed by Office of Naval research grant N00014-17-1-2552 and DGA Astrid project n◦ ANR-17-ASTR-0013-01.

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