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

Lifeng Zhang; Yajie Song

doi:doi:10.11648/j.jeee.20180602.12

| Peer-Reviewed

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

Lifeng Zhang, Yajie Song

Published in Journal of Electrical and Electronic Engineering (Volume 6, Issue 2)

Received: 19 June 2018 Published: 20 June 2018

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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.

Published in	Journal of Electrical and Electronic Engineering (Volume 6, Issue 2)
DOI	10.11648/j.jeee.20180602.12
Page(s)	46-52
Creative Commons	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), 2018. Published by Science Publishing Group

Keywords

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

References

[1]	H. S. Tapp, A. J. Peyton, E. K. Kemsley and R. H. Wilson, “Chemical engineering applications of electrical process tomography,” Sensors Actuat. B-Chem., 2003, vol. 92 pp. 17–24.
[2]	I. Ismail, J. C. Gamio, S. F. A. Bukhari, and W. Q. Yang, “Tomography for multi-phase flow measurement in the oil industry,” Flow Meas. Instrum., 2005, vol. 16 pp. 145–155.
[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.
[5]	W. Q. Yang and S. Liu, “Role of tomography in gas/solids flow measurement,” Flow Meas. Instrum., 2000, vol. 11 pp. 237–244.
[6]	J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell., 2009, vol. 31 pp. 210–227.
[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.
[10]	W. Q. Yang, D. M. Spink, T. A. York, and H. McCann, “Optimization of an iterative image reconstruction algorithm for electrical capacitance tomography,” Meas. Sci. Technol., 1999, vol. 10 pp. L37–L39.
[11]	D. L. Donoho, “Compressed sensing,” IEEE T. Inform. Theory, 2006, vol. 52 pp. 1289–1306.
[12]	S. S. Chen, D. L. Donoho, M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput., 2001, vol. 43 pp. 129–159.
[13]	M. A. T Figueiredo, R. D Nowak, S. J. Wright, “Gradient Projection for Sparse Reconstruction：Application to Compressed Sensing and Other Inverse Problem,” IEEE J-STSP, 2007, vol. 1 pp. 586–597.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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
Y1  - 2018/06/20
PY  - 2018
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  -

Copy | Download

Author Information

Lifeng Zhang

Department of Automation, North China Electric Power University, Baoding, China
Yajie Song

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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
Y1  - 2018/06/20
PY  - 2018
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  -

Copy | Download