Approximation BFGS methods for nonlinear image restoration

被引：10

作者：

Lu, Lin-Zhang ^{[4
,5
]}

Ng, Michael K. ^{[1
,2
]}

Lin, Fu-Rong ^{[3
]}

机构：

[1] Hong Kong Baptist Univ, Ctr Math Imaging & Vision, Kowloon Tong, Hong Kong, Peoples R China

[2] Hong Kong Baptist Univ, Dept Math, Kowloon Tong, Hong Kong, Peoples R China

[3] Shantou Univ, Dept Math, Shantou 515063, Guangdong, Peoples R China

[4] Guizhou Normal Univ, Sch Math & Comp Sci, Guiyang, Peoples R China

[5] Xiamen Univ, Sch Math Sci, Xiamen, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2009年 / 226卷 / 01期

关键词：

Nonlinear image restoration; Optimization; Regularization; QUASI-NEWTON METHODS; ALGORITHM; MINIMIZATION;

D O I：

10.1016/j.cam.2008.05.056

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We consider the iterative solution of unconstrained minimization problems arising from nonlinear image restoration. Our approach is based on a novel generalized BFGS method for such large-scale image restoration minimization problems. The complexity per step of the method is of O(n log n) operations and only O(n) memory allocations are required, where n is the number of image pixels. Based on the results given in [Carmine Di Fiore, Stefano Fanelli, Filomena Lepore, Paolo Zellini, Matrix algebras in quasi-Newton methods for unconstrained minimization, Numer. Math. 94 (2003) 479-500], we show that the method is globally convergent for our nonlinear image restoration problems. Experimental results are presented to illustrate the effectiveness of the proposed method. (c) 2008 Elsevier B.V. All rights reserved.

引用

页码：84 / 91

页数：8

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