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dc.contributor.authorSzirmay-Kalos, Laszloen_US
dc.contributor.authorTóth, Mártonen_US
dc.contributor.editorTheisel, Holger and Wimmer, Michaelen_US
dc.date.accessioned2021-04-09T18:20:27Z
dc.date.available2021-04-09T18:20:27Z
dc.date.issued2021
dc.identifier.isbn978-3-03868-133-5
dc.identifier.issn1017-4656
dc.identifier.urihttps://doi.org/10.2312/egs.20211017
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20211017
dc.description.abstractSurveillance cameras are expected to work also in bad visibility conditions, which requires algorithmic solutions to improve the captured image and to eliminate image degradation caused by these weather conditions. Algorithms for such tasks belong to the field of computational photography and have been successful in eliminating haze, fog, motion blur, etc. This paper presents a simple algorithm to suppress rain or snow from single images. The algorithm uses energy minimization, and we propose a novel data term and a Bregman distance based regularization term reflecting the particular properties of precipitation.en_US
dc.publisherThe Eurographics Associationen_US
dc.titleBregman Approach to Single Image De-Rainingen_US
dc.description.seriesinformationEurographics 2021 - Short Papers
dc.description.sectionheadersImaging and Video
dc.identifier.doi10.2312/egs.20211017
dc.identifier.pages33-36


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