Image Reconstruction with Component Regularization Based on Bregman Iteration

被引：0

作者：

Li, Xingxiu ^{[1
]}

Wei, Zhihui ^{[2
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Sci, Nanjing, Peoples R China

[2] Nanjing Univ Sci & Technol, Sch Engn & Comp Sci, Nanjing, Peoples R China

来源：

PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOLS 1-9 | 2009年

关键词：

bregman iteration; component regularization; image reconstruction;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Regularization technique is a common method for solving inverse problems in image processing. For a complex natural image which contains various structure components, it may be more effective to adopt different regularization terms for different components. An optimization model with component regularization, which was used to solve the problem of compressed sensing image reconstruction, has been established by us. In this paper, general linear inverse problems in imaging are considered, and an alternating iterative algorithm based on Bregman iteration is proposed to solve the optimization problem with component regularization. This iterative algorithm is applied to reconstruct natural images which contain piecewise smooth and texture components, in the compressed sensing framework. Experimental results show the effectiveness of the proposed algorithm.

引用

页码：755 / +

页数：2

共 13 条

[1]

[Anonymous], 0635 UCLA CAM

[2]

Burger M, 2006, COMMUN MATH SCI, V4, P179

[3]

Burger M, 2005, LECT NOTES COMPUT SC, V3752, P25

[4] Robust signal recovery from incomplete observations [J].

Candes, Emmanuel ;

Romberg, Justin .

2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS, 2006, :1281-+

[5]

Chan T, 2006, HANDBOOK OF MATHEMATICAL MODELS IN COMPUTER VISION, P17, DOI 10.1007/0-387-28831-7_2

[6] An iterative thresholding algorithm for linear inverse problems with a sparsity constraint [J].

Daubechies, I ;

Defrise, M ;

De Mol, C .

COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS, 2004, 57 (11) :1413-1457

[7]

GAN L, 2008, P EUSIPCO LAUS SWITZ

[8]

LI XX, ICIP 2009 UNPUB

[9] An iterative regularization method for total variation-based image restoration [J].

Osher, S ;

Burger, M ;

Goldfarb, D ;

Xu, JJ ;

Yin, WT .

MULTISCALE MODELING & SIMULATION, 2005, 4 (02) :460-489

[10]

PEYR G, LIT REV SPARSE OPTIM

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