Denoising an image by denoising its curvature image

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• dc.contributor.author Bertalmío, Marceloca
• dc.contributor.author Levine, Staceyca
• dc.date.accessioned 2016-06-29T07:38:18Z
• dc.date.available 2016-06-29T07:38:18Z
• dc.date.issued 2014ca
• dc.description.abstract In this article we argue that when an image is corrupted by additive noise, its curvature image is less affected by it, i.e. the PSNR of the curvature image is larger. We speculate that, given a denoising method, we may obtain better results by applying it to the curvature image and then reconstructing from it a clean image, rather than denoising the original image directly. Numerical experiments confirm this for several PDE-based and patch-based denoising algorithms.
• dc.description.sponsorship The first author acknowledges partial support by European Research Council, Starting Grant ref. 306337, and/nby Spanish grants AACC, ref. TIN2011-15954-E, and Plan Nacional, ref. TIN2012-38112. The second author was supported in part by NSF-DMS #0915219.
• dc.format.mimetype application/pdfca
• dc.identifier.citation Bertalmío M, Levine S. Denoising an image by denoising its curvature image. SIAM J Imaging Sc. 2014;7(1):187-211. DOI: 10.1137/120901246ca
• dc.identifier.doi http://dx.doi.org/10.1137/120901246
• dc.identifier.issn 1936-4954ca
• dc.identifier.uri http://hdl.handle.net/10230/26983
• dc.language.iso engca
• dc.publisher SIAM (Society for Industrial and Applied Mathematics)ca
• dc.relation.ispartof SIAM Journal on Imaging Sciences. 2014;7(1):187-211.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/306337ca
• dc.rights © Society for Industrial and Applied Mathematicsca
• dc.rights.accessRights info:eu-repo/semantics/openAccessca
• dc.subject.keyword Image denoising
• dc.subject.keyword Curvature
• dc.subject.keyword Image reconstruction
• dc.title Denoising an image by denoising its curvature imageca
• dc.type info:eu-repo/semantics/articleca
• dc.type.version info:eu-repo/semantics/acceptedVersionca