Volumetric Data Reduction in a Compressed Sensing Framework

In 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 volumet...

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Veröffentlicht in:Computer graphics forum Jg. 33; H. 3; S. 111 - 120
Hauptverfasser: Xu, X., Sakhaee, E., Entezari, A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.06.2014
Schlagworte:
Analysis
Categories and Subject Descriptors (according to ACM CCS)
Compressed
Computer graphics
Data reduction
Detection
Fourier analysis
I.4.10 [Image Processing and Computer Vision]: Image Representation-Volumetric
I.4.2 [Image Processing and Computer Vision]: Compression-Approximate methods
I.4.5 [Image Processing and Computer Vision]: Reconstruction-Transform methods
Image processing systems
Reconstruction
Representations
Studies
Visualization
Volumetric analysis
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:In 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.
Bibliographie:istex:943ECE8FA06BDD80D860A2249CFAB700BB8067EF
ArticleID:CGF12367
ark:/67375/WNG-XZ5TPB8X-4
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ObjectType-Feature-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12367