Image Deblurring Via Total Variation Based Structured Sparse Model Selection

Liyan Ma, Tieyong ZENG*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

15 Citations (Scopus)


In this paper, we study the image deblurring problem based on sparse representation over learned dictionary which leads to promising performance in image restoration in recent years. However, the commonly used overcomplete dictionary is not well structured. This shortcoming makes the approximation be unstable and demand much computational time. To overcome this, the structured sparse model selection (SSMS) over a family of learned orthogonal bases was proposed recently. In this paper, We further analyze the properties of SSMS and propose a model for deblurring under Gaussian noise. Numerical experimental results show that the proposed algorithm achieves competitive performance. As a generalization, we give a modified model for deblurring under salt-and-pepper noise. The resulting algorithm also has a good performance.

Original languageEnglish
JournalJournal of Scientific Computing
Issue number1
Publication statusPublished - 1 Apr 2016

Scopus Subject Areas

  • Software
  • Theoretical Computer Science
  • Numerical Analysis
  • Engineering(all)
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Dictionary learning
  • Image deblurring
  • Sparse representation
  • Variational method


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