Improved total variation minimization method for compressive sensing by intra-prediction

  • Authors:
  • Jie Xu;Jianwei Ma;Dongming Zhang;Yongdong Zhang;Shouxun Lin

  • Affiliations:
  • Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China and Graduate University of Chinese Academy of Sciences, Beijing 100190, China;Institute of Applied Mathematics, Harbin Institute of Technology, Harbin 150001, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

  • Venue:
  • Signal Processing
  • Year:
  • 2012

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Abstract

Total variation (TV) minimization algorithms are often used to recover sparse signals or images in the compressive sensing (CS). But the use of TV solvers often suffers from undesirable staircase effect. To reduce this effect, this paper presents an improved TV minimization method for block-based CS by intra-prediction. The new method conducts intra-prediction block by block in the CS reconstruction process and generates a residual for the image block being decoded in the CS measurement domain. The gradient of the residual is sparser than that of the image itself, which can lead to better reconstruction quality in CS by TV regularization. The staircase effect can also be eliminated due to effective reconstruction of the residual. Furthermore, to suppress blocking artifacts caused by intra-prediction, an efficient adaptive in-loop deblocking filter was designed for post-processing during the CS reconstruction process. Experiments show competitive performances of the proposed hybrid method in comparison with state-of-the-art TV models for CS with respect to peak signal-to-noise ratio and the subjective visual quality.