Image reconstruction algorithm based on compressed sensing for electrical capacitance tomography

Lifeng Zhang; Zhaolin Liu; Pei Tian

doi:10.1117/12.2244311

29 August 2016 Image reconstruction algorithm based on compressed sensing for electrical capacitance tomography

Lifeng Zhang, Zhaolin Liu, Pei Tian

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100333D (2016) https://doi.org/10.1117/12.2244311
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

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.

Citation Download Citation

Lifeng Zhang, Zhaolin Liu, and Pei Tian "Image reconstruction algorithm based on compressed sensing for electrical capacitance tomography", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100333D (29 August 2016); https://doi.org/10.1117/12.2244311

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