A Fast Algorithm for Constrained GLAD Estimation with Application to Image Restoration

被引：1
|
作者：
Xia, Youshen [1 ]
机构：
[1] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350002, Peoples R China
来源：
2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2010年
关键词：
GLAD estimate; constrained l(1)-norm problem; fast algorithm; image restoration; LINEAR CONSTRAINTS; PARAMETER; REGULARIZATION; MINIMIZATION; NOISE; NORM; L1;
D O I：
10.1109/WCICA.2010.5554050
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to relax need of the optimal regularization parameter to be estimated, a cooperative recurrent neural network (CRNN) algorithm for image restoration was presented by solving a generalized least absolute deviation ( GLAD) problem. This paper proposes a fast algorithm for solving a constrained l(1)-norm problem which contains the GLAD problem as its special case. The proposed iterative algorithm is guaranteed to converge globally to an optimal estimate under a fixed step length. Compared with the CRNN algorithm being continuous time, the proposed iterative algorithm has a fast convergence speed. Illustrative examples with application to image restoration show that the proposed iterative algorithm has a much faster convergence rate than the CRNN algorithm.
引用
收藏
页码：729 / 733
页数：5
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