Volumetric Data Reduction in a Compressed Sensing Framework
X. Xu, E. Sakhaee, A. Entezari
In Computer Graphics Forum, 33(3), 2014.
Abstract: 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.
Keyword(s): Categories and Subject Descriptors (according to ACM CCS), I.4.2 [Image Processing and Computer Vision]: Compression - Approximate methods, I.4.5 [Image Processing and Computer Vision]: Reconstruction - Transform methods, I.4.10 [Image Processing and Computer Vision]: Image Representation - Volumetric
Article URL: http://dx.doi.org/10.1111/cgf.12367
BibTeX format:
@article{Xu:2014:VDR,
  author = {X. Xu and E. Sakhaee and A. Entezari},
  title = {Volumetric Data Reduction in a Compressed Sensing Framework},
  journal = {Computer Graphics Forum},
  volume = {33},
  number = {3},
  pages = {111--120},
  year = {2014},
}
Search for more articles by X. Xu.
Search for more articles by E. Sakhaee.
Search for more articles by A. Entezari.

Return to the search page.


graphbib: Powered by "bibsql" and "SQLite3."