Ghimpeteanu, GabrielaKane, DavidBatard, ThomasLevine, StaceyBertalmío, Marcelo2019-05-282019-05-282016Ghimpeteanu 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.2016.7532932978-1-4673-9961-62381-8549http://hdl.handle.net/10230/41638Comunicació presentada a: IEEE International Conference on Image Processing 2016, celebrada del 25 l 28 de setembre de 2016 a Phoenix, Estats Units d'Amèrica.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.application/pdfeng© 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.2016.7532932info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/ICIP.2016.7532932Image denoisingCamera pipelineImage quality metricsPerceptual metricsPsychophysical experimentsinfo:eu-repo/semantics/openAccess