Total Variation Structured Total Least Squares Method for Image Restoration

Xi Le Zhao, Wei Wang, Tie Yong Zeng, Ting Zhu Huang, Michael K. Ng*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

72 Citations (Scopus)
29 Downloads (Pure)


In this paper, we study the total variation structured total least squares method for image restoration. In the image restoration problem, the point spread function is corrupted by errors. In the model, we study the objective function by minimizing two variables: the restored image and the estimated error of the point spread function. The proposed objective function consists of the data-fitting term containing these two variables, the magnitude of error and the total variation regularization of the restored image. By making use of the structure of the objective function, an efficient alternating minimization scheme is developed to solve the proposed model. Numerical examples are also presented to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme.

Original languageEnglish
Pages (from-to)B1304-B1320
Number of pages17
JournalSIAM Journal of Scientific Computing
Issue number6
Publication statusPublished - 5 Dec 2013

Scopus Subject Areas

  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Alternating minimization
  • Image restoration
  • Regularization
  • Structured total least squares
  • Total variation


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