Fusion-based variational image dehazing

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dc.contributor.authorGaldran, Adrian
dc.contributor.authorVazquez-Corral, Javier
dc.contributor.authorPardo, David
dc.contributor.authorBertalmio, Marcelo
dc.contributor.institutionTecnalia Research & Innovation
dc.date.accessioned2024-07-24T12:15:09Z
dc.date.available2024-07-24T12:15:09Z
dc.date.issued2017-02
dc.descriptionPublisher Copyright: © 1994-2012 IEEE.
dc.description.abstractWe propose a novel image-dehazing technique based on the minimization of two energy functionals and a fusion scheme to combine the output of both optimizations. The proposed fusion-based variational image-dehazing (FVID) method is a spatially varying image enhancement process that first minimizes a previously proposed variational formulation that maximizes contrast and saturation on the hazy input. The iterates produced by this minimization are kept, and a second energy that shrinks faster intensity values of well-contrasted regions is minimized, allowing to generate a set of difference-of-saturation (DiffSat) maps by observing the shrinking rate. The iterates produced in the first minimization are then fused with these DiffSat maps to produce a haze-free version of the degraded input. The FVID method does not rely on a physical model from which to estimate a depth map, nor it needs a training stage on a database of human-labeled examples. Experimental results on a wide set of hazy images demonstrate that FVID better preserves the image structure on nearby regions that are less affected by fog, and it is successfully compared with other current methods in the task of removing haze degradation from faraway regions.en
dc.description.statusPeer reviewed
dc.format.extent5
dc.identifier.citationGaldran , A , Vazquez-Corral , J , Pardo , D & Bertalmio , M 2017 , ' Fusion-based variational image dehazing ' , IEEE Signal Processing Letters , vol. 24 , no. 2 , 7792620 , pp. 151-155 . https://doi.org/10.1109/LSP.2016.2643168
dc.identifier.doi10.1109/LSP.2016.2643168
dc.identifier.issn1070-9908
dc.identifier.urihttps://hdl.handle.net/11556/4545
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85015167373&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Signal Processing Letters
dc.relation.projectIDHorizon 2020 Framework Programme, H2020, 644202
dc.relation.projectIDSeventh Framework Programme, FP7, 306337
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsColor correction
dc.subject.keywordscontrast enhancement
dc.subject.keywordsimage dehazing
dc.subject.keywordsimage fusion
dc.subject.keywordsvariational image processing
dc.subject.keywordsSignal Processing
dc.subject.keywordsElectrical and Electronic Engineering
dc.subject.keywordsApplied Mathematics
dc.titleFusion-based variational image dehazingen
dc.typejournal article
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