A Convex Variational Model for Restoring Blurred Images with Multiplicative Noise

被引:104
作者
Dong, Yiqiu [1 ]
Zeng, Tieyong [2 ]
机构
[1] Tech Univ Denmark, Dept Appl Math & Comp Sci, DK-2800 Lyngby, Denmark
[2] Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R China
关键词
convexity; deblurring; multiplicative noise; primal-dual algorithm; total variation regularization; variational model; TOTAL VARIATION MINIMIZATION; REMOVAL; ALGORITHMS; RECOVERY; POISSON;
D O I
10.1137/120870621
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, a new variational model for restoring blurred images with multiplicative noise is proposed. Based on the statistical property of the noise, a quadratic penalty function technique is utilized in order to obtain a strictly convex model under a mild condition, which guarantees the uniqueness of the solution and the stabilization of the algorithm. For solving the new convex variational model, a primal-dual algorithm is proposed, and its convergence is studied. The paper ends with a report on numerical tests for the simultaneous deblurring and denoising of images subject to multiplicative noise. A comparison with other methods is provided as well.
引用
收藏
页码:1598 / 1625
页数:28
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