Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern

Chun Shien Lu, Wei Jie Liang

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

5 Citations (Scopus)

Abstract

Compressive sensing of multi-dimensional signals (tensors) only receives limited attention. Separable sensing and proper sparsity pattern play two key roles for compressive sensing of tensors to be feasible and efficient. In view of inherent characteristic of 2D images and 3D videos, we propose the use of tree-structure sparsity pattern in tensor compressive sensing and develop a multiway tree-structure sparsity pattern OMP algorithm in this paper. Experimental results demonstrate the effectiveness of our method in terms of recovery quality and speed.

Original languageEnglish
Title of host publication2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages738-742
Number of pages5
ISBN (Electronic)9781479954032
DOIs
Publication statusPublished - 2014 Sep 3
Event2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Xi'an, China
Duration: 2014 Jul 92014 Jul 13

Publication series

Name2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings

Other

Other2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014
CountryChina
CityXi'an
Period14-07-0914-07-13

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing

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