Abstract

This letter proposes a novel compressive sensing reconstruction method for correlated images by using joint regularization, where a compensation-based adaptive total variation (CATV) regularization and a multi-image nonlocal low-rank (MNLR) regularization are included. In CATV, local weights are assigned to the residual values in the gradient domain so as to constrain the regularization strength at each pixel. In MNLR, the search of similar patches goes across different images so that both self-similarity and inter-image similarity are explored. Afterward, an efficient algorithm is proposed to solve the joint formulation, using a Split-Bregman-based technique. The effectiveness of the proposed approach is demonstrated with experiments on both multiview images and video sequences.

Original languageEnglish (US)
Article number7403894
Pages (from-to)449-453
Number of pages5
JournalIEEE Signal Processing Letters
Volume23
Issue number4
DOIs
StatePublished - Apr 1 2016

Keywords

  • Compressive Sensing
  • Motion/ Disparity Estimation
  • Nonlocal Low-rank Regularization
  • Total Variation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics

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