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dc.contributor.authorFarhadifard, Fahimehen_US
dc.contributor.authorRadolko, Martinen_US
dc.contributor.editorT. Bashford-Rogers and L. P. Santosen_US
dc.date.accessioned2016-04-26T07:56:10Z
dc.date.available2016-04-26T07:56:10Z
dc.date.issued2016en_US
dc.identifier.issn1017-4656en_US
dc.identifier.urihttp://dx.doi.org/10.2312/egsh.20161010en_US
dc.description.abstractWe present an adaptive underwater (UW) image deblurring algorithm based on sparse representation where a blur estimation is used to guide the algorithm for the best image reconstruction. The strong blur in this medium is caused by forward scatter and is challenging since it increases by camera scene distance. It is a common practice to use methods such as dark channel prior to estimate the depth map, and use this information to improve the image quality. However, we found it not successful in the case of blur since these methods are based on haze phenomenon. We propose a simple but effective algorithm via sparse representation which establishes a blur strength estimation and uses this information for adaptive deblurring. Extensive experiments manifest the effectiveness of our method in case of small but challenging blur changes.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.4.3 [Image processing and computer vision]en_US
dc.subjectEnhancementen_US
dc.titleAdaptive UW Image Deblurring via Sparse Representationen_US
dc.description.seriesinformationEG 2016 - Short Papersen_US
dc.description.sectionheadersImagingen_US
dc.identifier.doi10.2312/egsh.20161010en_US
dc.identifier.pages41-44en_US


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