Enhanced variational image dehazing

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 challeng...

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Authors: Galdran, Adrian, Vazquez-Corral, Javier, Pardo, David, Bertalmío, Marcelo
Format: article
Status:Versión aceptada para publicación
Publication Date:2015
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/26896
Online Access:http://hdl.handle.net/10230/26896
http://dx.doi.org/10.1137/15M1008889
Access Level:Open access
Keyword:Image dehazing
Perceptual color correction
Contrast enhancement
Variational image processing
Visibility enhancement
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spelling Enhanced variational image dehazingGaldran, AdrianVazquez-Corral, JavierPardo, DavidBertalmío, MarceloImage dehazingPerceptual color correctionContrast enhancementVariational image processingVisibility enhancementImages 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.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)”.SIAM (Society for Industrial and Applied Mathematics)201620162015info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/26896http://dx.doi.org/10.1137/15M1008889reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésSIAM Journal on Imaging Sciences. 2015;8(3):1519-46info:eu-repo/grantAgreement/EC/FP7/306337info:eu-repo/grantAgreement/ES/1PN/MTM2013-40824-P© Society for Industrial and Applied Mathematicsinfo:eu-repo/semantics/openAccessoai:recercat.cat:10230/268962026-05-29T05:05:01Z
dc.title.none.fl_str_mv Enhanced variational image dehazing
title Enhanced variational image dehazing
spellingShingle Enhanced variational image dehazing
Galdran, Adrian
Image dehazing
Perceptual color correction
Contrast enhancement
Variational image processing
Visibility enhancement
title_short Enhanced variational image dehazing
title_full Enhanced variational image dehazing
title_fullStr Enhanced variational image dehazing
title_full_unstemmed Enhanced variational image dehazing
title_sort Enhanced variational image dehazing
dc.creator.none.fl_str_mv Galdran, Adrian
Vazquez-Corral, Javier
Pardo, David
Bertalmío, Marcelo
author Galdran, Adrian
author_facet Galdran, Adrian
Vazquez-Corral, Javier
Pardo, David
Bertalmío, Marcelo
author_role author
author2 Vazquez-Corral, Javier
Pardo, David
Bertalmío, Marcelo
author2_role author
author
author
dc.subject.none.fl_str_mv Image dehazing
Perceptual color correction
Contrast enhancement
Variational image processing
Visibility enhancement
topic Image dehazing
Perceptual color correction
Contrast enhancement
Variational image processing
Visibility enhancement
description 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.
publishDate 2015
dc.date.none.fl_str_mv 2015
2016
2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/26896
http://dx.doi.org/10.1137/15M1008889
url http://hdl.handle.net/10230/26896
http://dx.doi.org/10.1137/15M1008889
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv SIAM Journal on Imaging Sciences. 2015;8(3):1519-46
info:eu-repo/grantAgreement/EC/FP7/306337
info:eu-repo/grantAgreement/ES/1PN/MTM2013-40824-P
dc.rights.none.fl_str_mv © Society for Industrial and Applied Mathematics
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © Society for Industrial and Applied Mathematics
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv SIAM (Society for Industrial and Applied Mathematics)
publisher.none.fl_str_mv SIAM (Society for Industrial and Applied Mathematics)
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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