Two new efficient iterative regularization methods for image restoration problems
type: Item
instance of: scholarly article
title: Two new efficient iterative regularization methods for image restoration problems
zbMATH Open document ID: 1371.68315
DOI: 10.1155/2013/129652
author: Chao Zhao
author: Ting-Zhu Huang
author: Xi-Le Zhao
author: Liang-Jian Deng
published in: Abstract and Applied Analysis
publication date: 2014-06-20T00:00:00Z
review text: Summary: Iterative regularization methods are efficient regularization tools for image restoration problems. The \(\mathrm{IDR}(s)\) 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 \(\mathrm{IDR}(s)\) and LSMR methods for image restoration problems. Comparative numerical experiments show that \(\mathrm{IDR}(s)\) 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.
Mathematics Subject Classification ID: 68U10
Mathematics Subject Classification ID: 65F10
zbMATH DE Number: 6306043
Wikidata QID: Q58915321
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describes a project that uses: corner.m
describes a project that uses: UTV
describes a project that uses: Regularization tools
describes a project that uses: LSMR
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/129652
OpenAlex ID: W2011180867
description: scientific article; zbMATH DE number 6306043