An iterative algorithm for L1-TV constrained regularization in image restoration

Chen, K. and Piccolomini, E. Loli and Zama, F. (2015) An iterative algorithm for L1-TV constrained regularization in image restoration. Journal of Physics: Conference Series, 657. 012009. ISSN 1742-6588 (

[thumbnail of Chen-etal-JPCS-2015-An-iterative-algorithm-for-L1-TV-constrained-regularization]
Text. Filename: Chen-etal-JPCS-2015-An-iterative-algorithm-for-L1-TV-constrained-regularization.pdf
Final Published Version
License: Creative Commons Attribution 3.0 logo

Download (1MB)| Preview


We consider the problem of restoring blurred images affected by impulsive noise. The adopted method restores the images by solving a sequence of constrained minimization problems where the data fidelity function is the ℓ1 norm of the residual and the constraint, chosen as the image Total Variation, is automatically adapted to improve the quality of the restored images. Although this approach is general, we report here the case of vectorial images where the blurring model involves contributions from the different image channels (cross channel blur). A computationally convenient extension of the Total Variation function to vectorial images is used and the results reported show that this approach is efficient for recovering nearly optimal images.