Abstract
We present a MATLAB package with implementations of several algebraic iterative
reconstruction methods for discretizations of inverse problems. These so-called row action
methods rely on semi-convergence for achieving the necessary regularization of the
problem. Two classes of methods are implemented: Algebraic Reconstruction Techniques
(ART) and Simultaneous Iterative Reconstruction Techniques (SIRT). In addition we provide
a few simplified test problems from medical and seismic tomography. For each iterative
method, a number of strategies are available for choosing the relaxation parameter and the
stopping rule. The relaxation parameter can be fixed, or chosen adaptively in each iteration;
in the former case we provide a new ‘‘training’’ algorithm that finds the optimal parameter
for a given test problem. The stopping rules provided are the discrepancy principle, the
monotone error rule, and the NCP criterion; for the first two methods ‘‘training’’ can be
used to find the optimal discrepancy parameter.
Original language | English |
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Journal | Journal of Computational and Applied Mathematics |
Volume | 236 |
Issue number | 8 |
Pages (from-to) | 2167-2178 |
ISSN | 0377-0427 |
DOIs | |
Publication status | Published - 2012 |
Bibliographical note
This work is part of the project CSI: Computational Science in Imaging, supported by grant no. 274-07-0065 from the Danish Research Council for Technology and Production Sciences.Keywords
- Semi-convergence
- Relaxation parameters
- ART methods
- SIRT methods
- Tomographic imaging
- Stopping rules