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A general extending and constraining procedure for linear iterative methods

Algebraic reconstruction techniques (ARTs), on both their successive and simultaneous formulations, have been developed since the early 1970s as efficient ‘row-action methods’ for solving the image-reconstruction problem in computerized tomography. In this respect, two important development directio...

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Bibliographic Details
Main Authors: Nicola, Aurelian (Author) , Petra, Stefania (Author) , Schnörr, Christoph (Author)
Format: Article (Journal)
Language:English
Published: January 15, 2012
In: International journal of computer mathematics
Year: 2012, Volume: 89, Issue: 2, Pages: 231-253
ISSN:1029-0265
DOI:10.1080/00207160.2011.634002
Online Access:Verlag, Volltext: http://dx.doi.org/10.1080/00207160.2011.634002
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Author Notes:Aurelian Nicola, Stefania Petra, Constantin Popa and Christoph Schnörr

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520 |a Algebraic reconstruction techniques (ARTs), on both their successive and simultaneous formulations, have been developed since the early 1970s as efficient ‘row-action methods’ for solving the image-reconstruction problem in computerized tomography. In this respect, two important development directions were concerned with, first, their extension to the inconsistent case of the reconstruction problem and, second, their combination with constraining strategies, imposed by the particularities of the reconstructed image. In the first part of this paper, we introduce extending and constraining procedures for a general iterative method of an ART type and we propose a set of sufficient assumptions that ensure the convergence of the corresponding algorithms. As an application of this approach, we prove that Cimmino's simultaneous reflection method satisfies this set of assumptions, and we derive extended and constrained versions for it. Numerical experiments with all these versions are presented on a head phantom widely used in the image reconstruction literature. We also consider hard thresholding constraining used in sparse approximation problems and apply it successfully to a 3D particle image-reconstruction problem. 
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