Volumetric Data Reduction in a Compressed Sensing Framework

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dc.contributor.authorXu, Xieen_US
dc.contributor.authorSakhaee, Elhamen_US
dc.contributor.authorEntezari, Alirezaen_US
dc.contributor.editorH. Carr, P. Rheingans, and H. Schumannen_US
dc.date.accessioned2015-03-03T12:34:33Z
dc.date.available2015-03-03T12:34:33Z
dc.date.issued2014en_US
dc.description.abstractIn this paper, we investigate compressed sensing principles to devise an in-situ data reduction framework for visualization of volumetric datasets. We exploit the universality of the compressed sensing framework and show that the proposed method offers a refinable data reduction approach for volumetric datasets. The accurate reconstruction is obtained from partial Fourier measurements of the original data that are sensed without any prior knowledge of specific feature domains for the data. Our experiments demonstrate the superiority of surfacelets for efficient representation of volumetric data. Moreover, we establish that the accuracy of reconstruction can further improve once a more effective basis for a sparser representation of the data becomes available.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12367en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12367en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleVolumetric Data Reduction in a Compressed Sensing Frameworken_US
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33-Issue 3
EuroVis14: Eurographics Conference on Visualization