Journal of Electrical and Electronic Engineering

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Application of Gradient Projection for Sparse Reconstruction to Compressed Sensing for Image Reconstruction of Electrical Capacitance Tomography

Received: 19 June 2018    Accepted:     Published: 20 June 2018
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Abstract

The compressed sensing algorithm based on gradient projection for spare reconstruction (CS-GPSR) is applied to electrical capacitance tomography (ECT) image reconstruction in this paper. First, using the orthogonal basis of FFT transformation, the grey signals of original images can be sparse. Secondly, the observation matrix of ECT system was designed by rearranging the exciting-measuring order, and the capacitance measurements and corresponding sensitivity matrix can be obtained. Finally, the reconstructed images can be obtained using CS-GPSR algorithm. Simulation experiments were carried out and the results showed that the reconstructed images with higher quality can be obtained using the presented CS-GPSR algorithm, compared with conventional linear back projection (LBP) and Landweber iterative algorithms.

DOI 10.11648/j.jeee.20180602.12
Published in Journal of Electrical and Electronic Engineering (Volume 6, Issue 2, April 2018)
Page(s) 46-52
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Electrical Capacitance Tomography, Image Reconstruction, Compressed Sensing, Gradient Projection for Sparse Reconstruction

References
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[3] H. X. Wang and L. F. Zhang, “Identification of two-phase flow regimes based on support vector machine and electrical capacitance tomography,” Meas. Sci. Technol., 2009, vol. 20 pp. 114007.
[4] G. McKenzie and P. Record, “Prognostic monitoring of aircraft wiring using electrical capacitive tomography,” Rev. Sci. Instrum., 2011, vol. 82 pp. 124705.
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[7] J. M. Ye, H. G. Wang, and W. Q. Yang a, “Image reconstruction for electrical capacitance tomography based on sparse representation,” IEEE T. Instrum. Meas., 2015, vol. 64 pp. 89–102.
[8] H. C. Wang, I. Fedchenia, S. L. Shishkin, A. Finn, L. L. Smith, and M. Colket, “Sparsity-inspired image reconstruction for electrical capacitance tomography,” Flow Meas. Instrum., 2015, vol. 43 pp. 59–71.
[9] L. F. Zhang, Z. L. Liu, and P. Tian, “Image reconstruction algorithm for electrical capacitance tomography based on compressed sensing,” Acta Electronica Sinica, 2017, vol. 45 pp. 353–358.
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  • APA Style

    Lifeng Zhang, Yajie Song. (2018). Application of Gradient Projection for Sparse Reconstruction to Compressed Sensing for Image Reconstruction of Electrical Capacitance Tomography. Journal of Electrical and Electronic Engineering, 6(2), 46-52. https://doi.org/10.11648/j.jeee.20180602.12

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    ACS Style

    Lifeng Zhang; Yajie Song. Application of Gradient Projection for Sparse Reconstruction to Compressed Sensing for Image Reconstruction of Electrical Capacitance Tomography. J. Electr. Electron. Eng. 2018, 6(2), 46-52. doi: 10.11648/j.jeee.20180602.12

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    AMA Style

    Lifeng Zhang, Yajie Song. Application of Gradient Projection for Sparse Reconstruction to Compressed Sensing for Image Reconstruction of Electrical Capacitance Tomography. J Electr Electron Eng. 2018;6(2):46-52. doi: 10.11648/j.jeee.20180602.12

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  • @article{10.11648/j.jeee.20180602.12,
      author = {Lifeng Zhang and Yajie Song},
      title = {Application of Gradient Projection for Sparse Reconstruction to Compressed Sensing for Image Reconstruction of Electrical Capacitance Tomography},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {6},
      number = {2},
      pages = {46-52},
      doi = {10.11648/j.jeee.20180602.12},
      url = {https://doi.org/10.11648/j.jeee.20180602.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20180602.12},
      abstract = {The compressed sensing algorithm based on gradient projection for spare reconstruction (CS-GPSR) is applied to electrical capacitance tomography (ECT) image reconstruction in this paper. First, using the orthogonal basis of FFT transformation, the grey signals of original images can be sparse. Secondly, the observation matrix of ECT system was designed by rearranging the exciting-measuring order, and the capacitance measurements and corresponding sensitivity matrix can be obtained. Finally, the reconstructed images can be obtained using CS-GPSR algorithm. Simulation experiments were carried out and the results showed that the reconstructed images with higher quality can be obtained using the presented CS-GPSR algorithm, compared with conventional linear back projection (LBP) and Landweber iterative algorithms.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Application of Gradient Projection for Sparse Reconstruction to Compressed Sensing for Image Reconstruction of Electrical Capacitance Tomography
    AU  - Lifeng Zhang
    AU  - Yajie Song
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    N1  - https://doi.org/10.11648/j.jeee.20180602.12
    DO  - 10.11648/j.jeee.20180602.12
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
    SP  - 46
    EP  - 52
    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20180602.12
    AB  - The compressed sensing algorithm based on gradient projection for spare reconstruction (CS-GPSR) is applied to electrical capacitance tomography (ECT) image reconstruction in this paper. First, using the orthogonal basis of FFT transformation, the grey signals of original images can be sparse. Secondly, the observation matrix of ECT system was designed by rearranging the exciting-measuring order, and the capacitance measurements and corresponding sensitivity matrix can be obtained. Finally, the reconstructed images can be obtained using CS-GPSR algorithm. Simulation experiments were carried out and the results showed that the reconstructed images with higher quality can be obtained using the presented CS-GPSR algorithm, compared with conventional linear back projection (LBP) and Landweber iterative algorithms.
    VL  - 6
    IS  - 2
    ER  - 

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Author Information
  • Department of Automation, North China Electric Power University, Baoding, China

  • Department of Automation, North China Electric Power University, Baoding, China

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