A variational framework for single image Dehazing

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dc.contributor.authorGaldran, Adrian
dc.contributor.authorVazquez-Corral, Javier
dc.contributor.authorPardo, David
dc.contributor.authorBertalmío, Marcelo
dc.contributor.editorRother, Carsten
dc.contributor.editorAgapito, Lourdes
dc.contributor.editorBronstein, Michael M.
dc.contributor.institutionTecnalia Research & Innovation
dc.date.accessioned2024-07-24T11:47:01Z
dc.date.available2024-07-24T11:47:01Z
dc.date.issued2015
dc.descriptionPublisher Copyright: © Springer International Publishing Switzerland 2015.
dc.description.abstractImages captured under adverse weather conditions, such as haze or fog, typically exhibit low contrast and faded colors, which may severely limit the visibility within the scene. Unveiling the image structure under the haze layer and recovering vivid colors out of a single image remains a challenging task, since the degradation is depth-dependent and conventional methods are unable to handle this problem. We propose to extend a well-known perception-inspired variational framework [1] for the task of single image dehazing. The main modification consists on the replacement of the value used by this framework for the grey-world hypothesis by an estimation of the mean of the clean image. This allows us to devise a variational method that requires no estimate of the depth structure of the scene, performing a spatially-variant contrast enhancement that effectively removes haze from far away regions. Experimental results show that our method competes well with other state-of-the-art methods in typical benchmark images, while outperforming current image dehazing methods in more challenging scenarios.en
dc.description.statusPeer reviewed
dc.format.extent12
dc.identifier.citationGaldran , A , Vazquez-Corral , J , Pardo , D & Bertalmío , M 2015 , A variational framework for single image Dehazing . in C Rother , L Agapito & M M Bronstein (eds) , Computer Vision - ECCV 2014 Workshops, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 8927 , Springer Verlag , pp. 259-270 , 13th European Conference on Computer Vision, ECCV 2014 , Zurich , Switzerland , 6/09/14 . https://doi.org/10.1007/978-3-319-16199-0_18
dc.identifier.citationconference
dc.identifier.doi10.1007/978-3-319-16199-0_18
dc.identifier.isbn9783319161983
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/11556/1620
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=84928806878&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherSpringer Verlag
dc.relation.ispartofComputer Vision - ECCV 2014 Workshops, Proceedings
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.projectIDSeventh Framework Programme, FP7, 306337
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsColor correction
dc.subject.keywordsContrast enhancement
dc.subject.keywordsImage defogging
dc.subject.keywordsImage dehazing
dc.subject.keywordsTheoretical Computer Science
dc.subject.keywordsGeneral Computer Science
dc.titleA variational framework for single image Dehazingen
dc.typeconference output
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