Iterative reconstruction algorithm for the inverse problems in electrical capacitance tomography

The electrical capacitance tomography (ECT) technology is a promising tomography technology that can image the distribution information of permittivity in a measurement region. The imaging algorithms play crucial roles in practical ECT image reconstruction problems. Different from previous numerical...

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Vydané v:Flow measurement and instrumentation Ročník 64; s. 204 - 212
Hlavní autori: Guo, Ge, Tong, Guowei, Lu, Lian, Liu, Shi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.12.2018
Predmet:
Electrical capacitance tomography
Image reconstruction
Split Bregman iteration algorithm
Total variation
Total variation
Split Bregman iteration algorithm
Electrical capacitance tomography
Image reconstruction
ISSN:0955-5986, 1873-6998
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Popis
Shrnutí:The electrical capacitance tomography (ECT) technology is a promising tomography technology that can image the distribution information of permittivity in a measurement region. The imaging algorithms play crucial roles in practical ECT image reconstruction problems. Different from previous numerical methods, this study proposes a novel cost function to model the ECT inverse problem, in which the L1-norm is used as the data fidelity to weaken the influence of the outliers contained in the capacitance data, the L1 regularization is introduced to enhance the sparseness of reconstructed objects, and the second order total variation (STV) regularization is used to weaken the staircasing effects caused by the first order total variation (FTV) regularization. The split Bregman iteration (SBI) algorithm that splits a complicated ECT imaging problem into several simpler sub-problems is developed to solve the proposed cost function effectively. The numerical simulation results show that the imaging algorithm proposed in this paper can reconstruct satisfactory results. •A new cost function is proposed to model the ECT imaging problem.•A new iterative algorithm is developed to effectively solve the cost function.•The feasibility and effectiveness of the algorithm are numerically validated.
ISSN:0955-5986
1873-6998
DOI:10.1016/j.flowmeasinst.2018.10.010