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	2016
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.citation	978-1-4673-9961-6
dc.identifier.citation	2381-8549
dc.identifier.citation	https://dx.doi.org/10.1109/ICIP.2016.7532932
dc.identifier.uri	http://hdl.handle.net/10230/41638
dc.format	application/pdf
dc.language.iso	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	2016 IEEE International Conference on Image Processing Proceedings; 2016 Sep 25-28; Phoenix, Arizona, USA. New York: IEEE; 2016. p. 3111-5.
dc.relation	info:eu-repo/grantAgreement/EC/FP7/306337
dc.relation	info:eu-repo/grantAgreement/ES/1PE7/TIN2015-71537-P
dc.rights	info:eu-repo/semantics/openAccess
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.subject	Image denoising
dc.subject	Camera pipeline
dc.subject	Image quality metrics
dc.subject	Perceptual metrics
dc.subject	Psychophysical experiments
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	info:eu-repo/semantics/acceptedVersion
dc.description.abstract	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.
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.
dc.description.abstract	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.

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