The conjugate gradient regularization method in computed tomography problems
type: Item
instance of: scholarly article
title: The conjugate gradient regularization method in computed tomography problems
zbMATH Open document ID: 0932.65138
DOI: 10.1016/S0096-3003(98)10007-3
author: E. Loli Piccolomini
author: F. Zama
published in: Applied Mathematics and Computation
publication date: 2000-03-21T00:00:00Z
review text: The authors solve inverse problems coming from the area of computed tomography by means of regularization methods based on conjugate gradient iterations. They develop a stopping criterion which is efficient for the computation of a regularized solution for the least squares normal equations. The stopping rule can be suitably applied also to the Tikhonov regularization method. They report computational experiments based on different physical models and with different degrees of noise. The authors compare the results obtained with those computed by other currently used methods such as algebraic reconstruction techniques and back projection.
reviewed by: R. S. Dahiya
Mathematics Subject Classification ID: 65R10
Mathematics Subject Classification ID: 44A12
Mathematics Subject Classification ID: 65R32
Mathematics Subject Classification ID: 92C55
zbMATH DE Number: 1311060
zbMATH Keywords: numerical examples
zbMATH Keywords: comparison of methods
zbMATH Keywords: inverse problems
zbMATH Keywords: computed tomography
zbMATH Keywords: regularization methods
zbMATH Keywords: conjugate gradient iterations
zbMATH Keywords: stopping criterion
zbMATH Keywords: Tikhonov regularization
describes a project that uses: Regularization tools
MaRDI profile type: Publication
cites work: Q3325518
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full work available at URL: s0096-3003(98)10007-3
OpenAlex ID: W2164676763
description: scientific article; zbMATH DE number 1311060