Enhanced variational image dehazing

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• dc.contributor.author Galdran, Adrianca
• dc.contributor.author Vazquez-Corral, Javierca
• dc.contributor.author Pardo, Davidca
• dc.contributor.author Bertalmío, Marceloca
• dc.date.accessioned 2016-06-10T08:50:29Z
• dc.date.available 2016-06-10T08:50:29Z
• dc.date.issued 2015ca
• dc.description.abstract Images obtained under adverse weather conditions, such as haze or fog, typically/nexhibit low contrast and faded colors, which may severely limit the visibility within the scene. Unveiling/nthe image structure under the haze layer and recovering vivid colors out of a single image/nremains a challenging task, since the degradation is depth-dependent and conventional methods are/nunable to overcome this problem. In this work, we extend a well-known perception-inspired variational/nframework for single image dehazing. Two main improvements are proposed. First, we replace/nthe value used by the framework for the grey-world hypothesis by an estimation of the mean of/nthe clean image. Second, we add a set of new terms to the energy functional for maximizing the/ninter-channel contrast. Experimental results show that the proposed Enhanced Variational Image/nDehazing (EVID) method outperforms other state-of-the-art methods both qualitatively and quantitatively./nIn particular, when the illuminant is uneven, our EVID method is the only one that recovers/nrealistic colors, avoiding the appearance of strong chromatic artifacts.
• dc.description.sponsorship D. Pardo was partially funded by the Project of the Spanish Ministry of Economy and Competitiveness with reference MTM2013-40824-P, the BCAM “Severo Ochoa” accreditation of excellence SEV-2013-0323, the CYTED 2011 project 712RT0449, and the Basque Government/nConsolidated Research Group Grant IT649-13 on “Mathematical Modeling, Simulation, and Industrial Applications (M2SI)”.
• dc.format.mimetype application/pdfca
• dc.identifier.citation Galdran A, Vazquez-Corral J, Pardo D, Bertalmío M. Enhanced variational image dehazing. SIAM J Imaging Sci. 2015;8(3):1519-46. DOI: 10.1137/15M1008889ca
• dc.identifier.doi http://dx.doi.org/10.1137/15M1008889
• dc.identifier.issn 1936-4954ca
• dc.identifier.uri http://hdl.handle.net/10230/26896
• dc.language.iso engca
• dc.publisher SIAM (Society for Industrial and Applied Mathematics)ca
• dc.relation.ispartof SIAM Journal on Imaging Sciences. 2015;8(3):1519-46
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/306337ca
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PN/MTM2013-40824-P
• dc.rights © Society for Industrial and Applied Mathematicsca
• dc.rights.accessRights info:eu-repo/semantics/openAccessca
• dc.subject.keyword Image dehazing
• dc.subject.keyword Perceptual color correction
• dc.subject.keyword Contrast enhancement
• dc.subject.keyword Variational image processing
• dc.subject.keyword Visibility enhancement
• dc.title Enhanced variational image dehazingca
• dc.type info:eu-repo/semantics/articleca
• dc.type.version info:eu-repo/semantics/acceptedVersionca