Application of Gradient Projection for Sparse Reconstruction to Compressed Sensing for Image Reconstruction of Electrical Capacitance Tomography
Journal of Electrical and Electronic Engineering
Volume 6, Issue 2, April 2018, Pages: 46-52
Received: Jun. 19, 2018;
Published: Jun. 20, 2018
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Lifeng Zhang, Department of Automation, North China Electric Power University, Baoding, China
Yajie Song, Department of Automation, North China Electric Power University, Baoding, China
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.
Application of Gradient Projection for Sparse Reconstruction to Compressed Sensing for Image Reconstruction of Electrical Capacitance Tomography, Journal of Electrical and Electronic Engineering.
Vol. 6, No. 2,
2018, pp. 46-52.
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.
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.
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.
G. McKenzie and P. Record, “Prognostic monitoring of aircraft wiring using electrical capacitive tomography,” Rev. Sci. Instrum., 2011, vol. 82 pp. 124705.
W. Q. Yang and S. Liu, “Role of tomography in gas/solids flow measurement,” Flow Meas. Instrum., 2000, vol. 11 pp. 237–244.
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.
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.
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.
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.
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.
D. L. Donoho, “Compressed sensing,” IEEE T. Inform. Theory, 2006, vol. 52 pp. 1289–1306.
S. S. Chen, D. L. Donoho, M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comput., 2001, vol. 43 pp. 129–159.
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.