Effective image block compressed sensing

Ying Hou, Yanning Zhang

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

7 Scopus citations

Abstract

In this paper, we propose an effective block compressed sensing algorithm using projected Land Weber based on bivariate shrinkage (BCS PL-BS) for natural images, and an improved noise variance estimation method is presented by using soft-thresholding bivariate shrinkage model for wavelet-based image denoising, which can more effectively remove noise and achieve better image reconstruction quality. Futhermore, the BCS PL-BS algorithm based on DPCM quantization is depthly studied. Experimental results demonstrate that the reconstruction performances of the proposed algorithm significantly outperform those of several state-of-the-art compressed sensing algorithms.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1085-1090
Number of pages6
ISBN (Electronic)9781479952083
DOIs
StatePublished - 4 Dec 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference22nd International Conference on Pattern Recognition, ICPR 2014
Country/TerritorySweden
CityStockholm
Period24/08/1428/08/14

Keywords

  • Bivariate shrinkage
  • Compressed sensing
  • Projected Landweber
  • Wavelet transform

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