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Proceedings Paper

Image reconstruction algorithm based on compressed sensing for electrical capacitance tomography
Author(s): Lifeng Zhang; Zhaolin Liu; Pei Tian
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Paper Abstract

In order to improve the sampling rate and the quality of reconstructed images of electrical capacitance tomography (ECT) system, a new ECT image reconstruction algorithm based on compressed sensing (CS) theory is proposed. Firstly, using discrete Fourier orthogonal basis, the original image gray signal can be transformed into a sparse signal. Then, the electrodes are excited randomly and the capacitance values of different electrode pairs are also measured in a random order. Thus, the capacitance signals and the corresponding observation matrix are obtained. After that, using L1 regularization model and primal dual interior point method, the reconstruction of original gray image can be obtained. Finally, the simulation experiments are performed. Simulation results have shown that the relative error of the reconstructed images obtained by the proposed method is smaller than the corresponding images obtained by the LBP algorithm and the Landweber algorithm.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100333D (29 August 2016); doi: 10.1117/12.2244311
Show Author Affiliations
Lifeng Zhang, North China Electric Power Univ. (China)
Zhaolin Liu, North China Electric Power Univ. (China)
Pei Tian, North China Electric Power Univ. (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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