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dc.contributor.authorGaldran, Adrian
dc.contributor.authorBria, Alessandro
dc.contributor.authorAlvarez-Gila, Aitor
dc.contributor.authorVazquez-Corral, Javier
dc.contributor.authorBertalmío, Marcelo
dc.date.accessioned2019-03-21T15:47:58Z
dc.date.available2019-03-21T15:47:58Z
dc.date.issued2018
dc.identifier.citationA. Galdran, A. Bria, A. Alvarez-Gila, J. Vazquez-Corral, and M. Bertalmío, “On the Duality Between Retinex and Image Dehazing,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 8212–8221.en
dc.identifier.isbn978-1-5386-6420-9en
dc.identifier.issn1063-6919en
dc.identifier.urihttp://hdl.handle.net/11556/694
dc.description.abstractImage dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem.en
dc.language.isoengen
dc.publisherIEEE Computer Societyen
dc.titleOn the Duality Between Retinex and Image Dehazingen
dc.typeconferenceObjecten
dc.identifier.doi10.1109/CVPR.2018.00857en
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsRetinexen
dc.subject.keywordsDehazingen
dc.subject.keywordsImage processingen
dc.journal.titleProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognitionen
dc.page.final8221en
dc.page.initial8212en
dc.conference.title31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018; Salt Lake City; United States; 18 June 2018 through 22 June 2018en


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