A review of image reconstruction methods in electrical capacitance tomography
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Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
J. Math. Comput. Sci.
Abstract
In this paper, we review image reconstruction methods and their suitability in electrical capacitance
tomography measurement system. These methods can be grouped into direct and iterative methods. Direct meth-
ods include Linear back projection, Singular value decomposition, and Tikhonov regularization. Iterative methods
are further divided into algebraic and optimization methods. Algebraic reconstruction methods include itera-
tive linear back projection, iterative Tikhonov, Landweber iteration, simultaneously algebraic reconstruction, and
model−based reconstruction. Optimization methods include fuzzy mathematical modeling, genetic algorithms,
artificial neural networks, generalized vector sampled pattern matching, total variation regularization, regularized
total least squares, extended Tikhonov regularization, simulated annealing, compressed sensing principle, popu-
lation entropy, adaptive differential evolution, least−squares support vector machine, and self-adaptive particle
swarm optimization. Some of these methods have been examined through experiments and their comparative
analysis have been given. Results show that iterative methods generate high quality images compared with non-
iterative ones when evaluated over full component fraction range. However, iterative methods are computationally
expensive, and hence used for research and off-line investigations rather than for on-line process monitoring
Sustainable Development Goals
A research article was submitted to Journal of Mathematical and Computational Science volume 6 (2016)
Keywords
Electrical capacitance tomography, Inverse problem, Image reconstruction