Block-based variable density compressed image sampling

Wei Qiao, Bin Liu, Zixiang Xiong, Gonzalo R. Arce, Javier Garcia-Frias, Wenwu Zhu, Zhisheng Yan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


Compressed sampling (CS) is a technique that enables signal reconstruction at sub-Nyquist sampling rate. A key problem in CS is how to design the sampling scheme. In this paper, we propose a novel sampling method for compressed image sampling, which exploits a priori information and uses a block-based strategy to improve image reconstruction. Our block-based sampling scheme assigns more samples to blocks with more high-frequency contents while making sure that important coefficients of each block are sampled. Simulation results show that our proposed method outperforms existing methods on both reconstruction quality and running time.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Print)9781467325332
Publication statusPublished - 2012
Externally publishedYes
EventIEEE International Conference on Image Processing 2012 - Coronado Springs - Disney World, Orlando, United States of America
Duration: 30 Sep 20123 Oct 2012
Conference number: 19th (Proceedings)


ConferenceIEEE International Conference on Image Processing 2012
Abbreviated titleICIP 2012
CountryUnited States of America
Internet address


  • block-based
  • Compressed sensing
  • image reconstruction
  • variable density sampling

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