Sparsity-based image deblurring with locally adaptive and nonlocally robust regularization

Weisheng Dong, Xin Li, Lei Zhang, Guangming Shi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)


Important structures in photographic images such as edges and textures are jointly characterized by local variation and nonlocal invariance (similarity). Both of them provide valuable heuristics to the regularization of image restoration process. In this paper, we propose to explore two sets of complementary ideas: 1) locally learn PCA-based dictionaries and estimate the sparsity regularization parameters for each coefficient; and 2) nonlocally enforce the invariance constraint by introducing a patch-similarity based term into the cost functional. The minimization of this new cost functional leads to an iterative thresholding-based image deblurring algorithm and its efficient implementation is discussed. Our experimental results have shown that the proposed scheme significantly outperforms several leading deblurring techniques in the literature on both objective and visual quality assessments.
Original languageEnglish
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Number of pages4
Publication statusPublished - 1 Dec 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: 11 Sep 201114 Sep 2011


Conference2011 18th IEEE International Conference on Image Processing, ICIP 2011


  • Image deblurring
  • iterative shrinkage
  • nonlocal similarity
  • sparsity-based local adaptation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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