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

5th International Workshop on New Computational Methods for Inverse Problems

物理学;计算机科学

Chen, K.^1 ; Piccolomini, E Loli^2 ; Zama, F.^2

Department of Mathematics, University of Liverpool, United Kingdom^1

Department of Mathematics, University of Bologna, Italy^2

关键词: Constrained minimization problem;    Convenient extensions;    Data fidelity;    Image channels;    Iterative algorithm;    Optimal images;    Total variation;    Vectorial images;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/657/1/012009/pdf
DOI  :  10.1088/1742-6596/657/1/012009

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

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 1norm 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.