A variational framework for single image dehazing

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• dc.contributor.author Galdran, Adrian
• dc.contributor.author Vazquez-Corral, Javier
• dc.contributor.author Pardo, David
• dc.contributor.author Bertalmío, Marcelo
• dc.date.accessioned 2019-05-31T16:45:50Z
• dc.date.available 2019-05-31T16:45:50Z
• dc.date.issued 2015
• dc.description Comunicació presentada a: European Conference on Computer Vision Workshops (ECCV 2014), celebrada del 6 al 7 de setembre de 2014 a Zurich, Suïssa.
• dc.description.abstract Images 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 struc- ture 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 frame- work [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.sponsorship JVC and MB were supported by European Research Council, Starting Grant ref. 306337, and by Spanish grants ref. TIN2011-15954-E and ref. TIN2012-38112.
• dc.format.mimetype application/pdf
• dc.identifier.citation Galdran A, Vazquez-Corral J, Pardo D, Bertalmío M. A variational framework for single image dehazing. In: Agapito L, Bronstein MM, Rother C, editors. European Conference on Computer Vision Workshops Part III (ECCV 2014); 2014 Sept 6-7; Zurich, Switzerland. Cham: Springer Verlag; 2015. p.259-70. (LNCS; no. 8927). DOI: 10.1007/978-3-319-16199-0_18
• dc.identifier.doi http://dx.doi.org/10.1007/978-3-319-16199-0_18
• dc.identifier.isbn 978-3-319-16198-3
• dc.identifier.issn 0302-9743
• dc.identifier.uri http://hdl.handle.net/10230/41680
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/306337
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/TIN2012-38112
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/TIN2011-15954-E
• dc.rights © Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16199-0_18
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Image dehazingen
• dc.subject.keyword Image defoggingen
• dc.subject.keyword Color correctionen
• dc.subject.keyword Contrast enhancementen
• dc.title A variational framework for single image dehazingen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion