An alternating iterative algorithm for image deblurring and denoising problems

被引：19

作者：

Wang, Si ^{[1
]}

Huang, Ting-Zhu ^{[1
]}

Liu, Jun ^{[1
]}

Lv, Xiao-Guang ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Math Sci, Inst Computat Sci, Chengdu 611731, Sichuan, Peoples R China

来源：

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2014年 / 19卷 / 03期

关键词：

Deblurring; Denoising; Total variation; Alternating iterative algorithm; Fast iterative shrinkage-thresholding algorithm; l(1) Regularization; TOTAL VARIATION MINIMIZATION; LINEAR INVERSE PROBLEMS; THRESHOLDING ALGORITHM; RESTORATION; RECONSTRUCTION; MODELS;

D O I：

10.1016/j.cnsns.2013.07.004

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, a modified l(1) minimization model for image delurring and denoising problems is considered. To solve the proposed l(1) minimization model, we present an efficient alternative iterative algorithm in which the fast iterative shrinkge-thresholding method (FISTA) and the well known dual approach for solving the denosing problems are alternately employed. Besides, we prove the convergence of the proposed algorithm. Numerical results demonstrate the efficiency and viability of the proposed algorithm to restore the degraded images. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：617 / 626

页数：10

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