Local denoising based on curvature smoothing can visually outperform non-local methods on photographs with actual noise

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• dc.contributor.author Ghimpeteanu, Gabriela
• dc.contributor.author Kane, David
• dc.contributor.author Batard, Thomas
• dc.contributor.author Levine, Stacey
• dc.contributor.author Bertalmío, Marcelo
• dc.date.accessioned 2019-05-28T13:01:05Z
• dc.date.available 2019-05-28T13:01:05Z
• dc.date.issued 2016
• dc.description Comunicació presentada a: IEEE International Conference on Image Processing 2016, celebrada del 25 l 28 de setembre de 2016 a Phoenix, Estats Units d'Amèrica.ca
• dc.description.abstract We propose a fast, local denoising method where the Euclidean curvature of the noisy image is approximated in a regularizing manner and a clean image is reconstructed from this smoothed curvature. User preference tests show that when denoising real photographs with actual noise our method produces results with the same visual quality as the more sophisticated, nonlocal algorithms Non-local Means and BM3D, but at a fraction of their computational cost. These tests also highlight the limitations of objective image quality metrics like PSNR and SSIM, which correlate poorly with user preference.en
• dc.description.sponsorship This work was supported by the European Research Council, Starting Grant ref. 306337, by the Spanish government, grant ref. TIN2015-71537-P, and by the Icrea Academia Award.
• dc.format.mimetype application/pdf
• dc.identifier.citation Ghimpeteanu G, Kane D, Batard T, Levine S, Bertalmío M. 2016. Local denoising based on curvature smoothing can visually outperform non-local methods on photographs with actual noise. In: 2016 IEEE International Conference on Image Processing Proceedings; 2016 Sep 25-28; Phoenix, Arizona, USA. New York: IEEE; 2016. p. 3111-5. DOI: 10.1109/ICIP.2016.7532932
• dc.identifier.doi http://dx.doi.org/10.1109/ICIP.2016.7532932
• dc.identifier.isbn 978-1-4673-9961-6
• dc.identifier.issn 2381-8549
• dc.identifier.uri http://hdl.handle.net/10230/41638
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof 2016 IEEE International Conference on Image Processing Proceedings; 2016 Sep 25-28; Phoenix, Arizona, USA. New York: IEEE; 2016. p. 3111-5.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/306337
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE7/TIN2015-71537-P
• dc.rights © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/ICIP.2016.7532932
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Image denoisingen
• dc.subject.keyword Camera pipelineen
• dc.subject.keyword Image quality metricsen
• dc.subject.keyword Perceptual metricsen
• dc.subject.keyword Psychophysical experimentsen
• dc.title Local denoising based on curvature smoothing can visually outperform non-local methods on photographs with actual noise
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion