Abstract and Applied Analysis

Two New Efficient Iterative Regularization Methods for Image Restoration Problems

Chao Zhao, Ting-Zhu Huang, Xi-Le Zhao, and Liang-Jian Deng

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Abstract

Iterative regularization methods are efficient regularization tools for image restoration problems. The IDR( s ) and LSMR methods are state-of-the-arts iterative methods for solving large linear systems. Recently, they have attracted considerable attention. Little is known of them as iterative regularization methods for image restoration. In this paper, we study the regularization properties of the IDR( s ) and LSMR methods for image restoration problems. Comparative numerical experiments show that IDR( s ) can give a satisfactory solution with much less computational cost in some situations than the classic method LSQR when the discrepancy principle is used as a stopping criterion. Compared to LSQR, LSMR usually produces a more accurate solution by using the L -curve method to choose the regularization parameter.

Article information

Source
Abstr. Appl. Anal., Volume 2013 (2013), Article ID 129652, 9 pages.

Dates
First available in Project Euclid: 27 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1393511953

Digital Object Identifier
doi:10.1155/2013/129652

Mathematical Reviews number (MathSciNet)
MR3073472

Zentralblatt MATH identifier
1371.68315

Citation

Zhao, Chao; Huang, Ting-Zhu; Zhao, Xi-Le; Deng, Liang-Jian. Two New Efficient Iterative Regularization Methods for Image Restoration Problems. Abstr. Appl. Anal. 2013 (2013), Article ID 129652, 9 pages. doi:10.1155/2013/129652. https://projecteuclid.org/euclid.aaa/1393511953


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