Iterative constrained minimization for vectorial TV image deblurring

K. Chen, E. Loli Piccolomini*, F. Zama

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In this paper, we consider the problem of restoring blurred noisy vectorial images where the blurring model involves contributions from the different image channels (cross-channel blur). The proposed method restores the images by solving a sequence of quadratic constrained minimization problems where the constraint is automatically adapted to improve the quality of the restored images. In the present case, the constraint is the Total Variation extended to vectorial images, and the objective function is the ℓ2 norm of the residual. After proving the convergence of the iterative method, we report the results obtained on a wide set of test images, showing that this approach is efficient for recovering nearly optimal results.
Original languageEnglish
Pages (from-to)240-255
Number of pages16
JournalJournal of Mathematical Imaging and Vision
Issue number2
Early online date14 Sept 2015
Publication statusPublished - 28 Feb 2016
Externally publishedYes


  • constrained minimization
  • deblurring
  • regularization algorithm
  • vectorial images
  • vectorial total variation


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