Imaging via three-dimensional compressive sampling (3DCS)

Xianbiao Shu, Narendra Ahuja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Compressive sampling (CS) aims at acquiring a signal at a sampling rate that is significantly below the Nyquist rate. Its main idea is that a signal can be decoded from incomplete linear measurements by seeking its sparsity in some domain. Despite the remarkable progress in the theory of CS, little headway has been made in the compressive imaging (CI) camera. In this paper, a three-dimensional compressive sampling (3DCS) approach is proposed to reduce the required sampling rate of the CI camera to a practical level. In 3DCS, a generic three-dimensional sparsity measure (3DSM) is presented, which decodes a video from incomplete samples by exploiting its 3D piecewise smoothness and temporal low-rank property. In addition, an efficient decoding algorithm is developed for this 3DSM with guaranteed convergence. The experimental results show that our 3DCS requires a much lower sampling rate than the existing CS methods without compromising recovery accuracy.

Original languageEnglish (US)
Title of host publication2011 International Conference on Computer Vision, ICCV 2011
Pages439-446
Number of pages8
DOIs
StatePublished - Dec 1 2011
Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
Duration: Nov 6 2011Nov 13 2011

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Other

Other2011 IEEE International Conference on Computer Vision, ICCV 2011
CountrySpain
CityBarcelona
Period11/6/1111/13/11

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ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Shu, X., & Ahuja, N. (2011). Imaging via three-dimensional compressive sampling (3DCS). In 2011 International Conference on Computer Vision, ICCV 2011 (pp. 439-446). [6126273] (Proceedings of the IEEE International Conference on Computer Vision). https://doi.org/10.1109/ICCV.2011.6126273