Fusion-based variational image dehazing

We 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...

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Authors: Galdran, A., Vazquez-Corral, J., Pardo, D., Bertalmio, M.
Format: article
Status:Published version
Publication Date:2017
Country:España
Institution:Basque Center for Applied Mathematics (BCAM)
Repository:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/659
Online Access:http://hdl.handle.net/20.500.11824/659
Access Level:Open access
Keyword:Color correction
contrast enhancement
image dehazing
image fusion
variational image processing
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spelling Fusion-based variational image dehazingGaldran, A.Vazquez-Corral, J.Pardo, D.Bertalmio, M.Color correctioncontrast enhancementimage dehazingimage fusionvariational image processingWe 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.201720172017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/659reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)Ingléshttp://ieeexplore.ieee.org/document/7792620/Reconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/6592026-06-19T12:47:47Z
dc.title.none.fl_str_mv Fusion-based variational image dehazing
title Fusion-based variational image dehazing
spellingShingle Fusion-based variational image dehazing
Galdran, A.
Color correction
contrast enhancement
image dehazing
image fusion
variational image processing
title_short Fusion-based variational image dehazing
title_full Fusion-based variational image dehazing
title_fullStr Fusion-based variational image dehazing
title_full_unstemmed Fusion-based variational image dehazing
title_sort Fusion-based variational image dehazing
dc.creator.none.fl_str_mv Galdran, A.
Vazquez-Corral, J.
Pardo, D.
Bertalmio, M.
author Galdran, A.
author_facet Galdran, A.
Vazquez-Corral, J.
Pardo, D.
Bertalmio, M.
author_role author
author2 Vazquez-Corral, J.
Pardo, D.
Bertalmio, M.
author2_role author
author
author
dc.subject.none.fl_str_mv Color correction
contrast enhancement
image dehazing
image fusion
variational image processing
topic Color correction
contrast enhancement
image dehazing
image fusion
variational image processing
description We 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.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/659
url http://hdl.handle.net/20.500.11824/659
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://ieeexplore.ieee.org/document/7792620/
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
reponame_str BIRD. BCAM's Institutional Repository Data
collection BIRD. BCAM's Institutional Repository Data
repository.name.fl_str_mv
repository.mail.fl_str_mv
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