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2013 Two New Efficient Iterative Regularization Methods for Image Restoration Problems
Chao Zhao, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng
Abstr. Appl. Anal. 2013: 1-9 (2013). DOI: 10.1155/2013/129652


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.


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Chao Zhao. Ting-Zhu Huang. Xi-Le Zhao. Liang-Jian Deng. "Two New Efficient Iterative Regularization Methods for Image Restoration Problems." Abstr. Appl. Anal. 2013 1 - 9, 2013.


Published: 2013
First available in Project Euclid: 27 February 2014

zbMATH: 1371.68315
MathSciNet: MR3073472
Digital Object Identifier: 10.1155/2013/129652

Rights: Copyright © 2013 Hindawi

Vol.2013 • 2013
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