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dc.contributor.authorMiandji, Ehsanen_US
dc.contributor.authorKronander, Joelen_US
dc.contributor.authorUnger, Jonasen_US
dc.contributor.editorOlga Sorkine-Hornung and Michael Wimmeren_US
dc.date.accessioned2015-04-16T07:43:06Z
dc.date.available2015-04-16T07:43:06Z
dc.date.issued2015en_US
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.12539en_US
dc.description.abstractWe present a new compressed sensing framework for reconstruction of incomplete and possibly noisy images and their higher dimensional variants, e.g. animations and light-fields. The algorithm relies on a learning-based basis representation. We train an ensemble of intrinsically two-dimensional (2D) dictionaries that operate locally on a set of 2D patches extracted from the input data. We show that one can convert the problem of 2D sparse signal recovery to an equivalent 1D form, enabling us to utilize a large family of sparse solvers. The proposed framework represents the input signals in a reduced union of subspaces model, while allowing sparsity in each subspace. Such a model leads to a much more sparse representation than widely used methods such as K-SVD. To evaluate our method, we apply it to three different scenarios where the signal dimensionality varies from 2D (images) to 3D (animations) and 4D (light-fields). We show that our method outperforms state-of-the-art algorithms in computer graphics and image processing literature.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.7 [Computer Graphics]en_US
dc.subjectThree Dimensional Graphics and Realismen_US
dc.subjectColoren_US
dc.subjectshadingen_US
dc.subjectshadowingen_US
dc.subjectand textureen_US
dc.titleCompressive Image Reconstruction in Reduced Union of Subspacesen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.sectionheadersSampling & Skinsen_US
dc.description.volume34en_US
dc.description.number2en_US
dc.identifier.doi10.1111/cgf.12539en_US
dc.identifier.pages033-044en_US


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