Reflection Separation in Light Fields based on Sparse Coding and Specular Flow

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dc.contributor.authorSulc, Antoninen_US
dc.contributor.authorAlperovich, Annaen_US
dc.contributor.authorMarniok, Nicoen_US
dc.contributor.authorGoldluecke, Bastianen_US
dc.contributor.editorMatthias Hullin and Marc Stamminger and Tino Weinkaufen_US
dc.date.accessioned2016-10-10T08:04:41Z
dc.date.available2016-10-10T08:04:41Z
dc.date.issued2016
dc.description.abstractWe present a method to separate a dichromatic reflection component from diffuse object colors for the set of rays in a 4D light field such that the separation is consistent across all subaperture views. The separation model is based on explaining the observed light field as a sparse linear combination of a constant-color specular term and a small finite set of albedos. Consistency across the light field is achieved by embedding the ray-wise separation into a global optimization framework. On each individual epipolar plane image (EPI), the diffuse coefficients need to be constant along lines which are the projections of the same scene point, while the specular coefficient needs to be constant along the direction of the specular flow within the epipolar volume. We handle both constraints with depth-dependent anisotropic regularizers, and demonstrate promising performance on a number of real-world light fields captured with a Lytro Illum plenoptic camera.en_US
dc.description.sectionheadersLight Fields and Materials
dc.description.seriesinformationVision, Modeling & Visualization
dc.identifier.doi10.2312/vmv.20161352
dc.identifier.isbn978-3-03868-025-3
dc.identifier.issn-
dc.identifier.pages137-144
dc.identifier.urihttps://doi.org/10.2312/vmv.20161352
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20161352
dc.publisherThe Eurographics Associationen_US
dc.subjectI.4.4 [Image Processing and Computer Vision]
dc.subjectRestoration
dc.subjectI.4.8 [Image Processing and Computer Vision]
dc.subjectScene Analysis
dc.subjectColor
dc.titleReflection Separation in Light Fields based on Sparse Coding and Specular Flowen_US
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