Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Sparsity Adaptive Compressed Sensing

WU Xinjie; YAN Shiyu; XU Panfeng; YAN Hua

doi:10.11999/JEIT170794

Volume 40 Issue 5

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(5): 1250-1257

WU Xinjie, YAN Shiyu, XU Panfeng, YAN Hua. Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Sparsity Adaptive Compressed Sensing[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1250-1257. doi: 10.11999/JEIT170794

Citation:

WU Xinjie, YAN Shiyu, XU Panfeng, YAN Hua. Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Sparsity Adaptive Compressed Sensing[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1250-1257. doi: 10.11999/JEIT170794

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Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Sparsity Adaptive Compressed Sensing

doi: 10.11999/JEIT170794

1.
(College of Physics, Liaoning University, Shenyang 110036, China)
2.
(School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China)

Funds:

The National Natural Science Foundation of China (61071141), The Scientific Research Foundation of Liaoning Province (20102082), The Scientific Research Project of Liaoning Provincial Education Department (LFW201708)

Received Date: 2017-08-07
Rev Recd Date: 2018-01-10
Publish Date: 2018-05-19

Abstract

Abstract

In order to improve quality of the reconstructed images of the Electrical Capacitance Tomography (ECT) system, an improved sparsity adaptive matching pursuit compressed sensing algorithm is proposed. Based on the coherence point of Compressed Sensing (CS) theory and ECT, the CS-ECT model is established. In the model, the sensitivity matrix of ECT is designed in a random order to be the observation matrix, the discrete cosine base is used as the sparse base, the capacitance value is measured as the observed value. By using the Linear Back Projection (LBP) algorithm, the sparsity of the estimated images is confirmed. The sparsity can be served as the initial value of the atomic index for sparsity adaptive iteration. The lack of image reconstruction accuracy caused by the inaccurate estimate of sparsity can be solved by the improved sparsity adaptive matching pursuit algorithm. Simulation results indicate that reconstructed images with higher accuracy can be obtained using the improved sparsity adaptive matching pursuit compressed sensing algorithm than the LBP algorithm, Landweber algorithm and Tikhonov algorithm. A new method of ECT reconstruction is provided.
- Image reconstruction,
- Electrical Capacitance Tomography (ECT),
- Sparsity adaptive,
- Compressed Sensing (CS)

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References(28)

References

WANG Huaxiang. Electrical Tomography[M]. Beijing: Science Press, 2013: 4-6.

王化祥. 电学层析成像[M]. 北京: 科学出版社, 2013: 4-6.

吴新杰, 何在刚, 李惠强, 等. 利用多准则Hopfield网络对ECT进行图像重建[J]. 电机与控制学报, 2016, 20(8): 98-104. doi: 10.15938/j.emc.2016.08.013.

WU Xinjie, HE Zaigang, LI Huiqiang, et al. Image reconstruction by using multi-criteria of hopfield network for ECT[J]. Electric Machines and Control, 2016, 20(8): 98-104. doi: 10.15938/j.emc.2016.08.013.

张立峰, 刘昭麟, 田沛. 基于压缩感知的电容层析成像图像重建算法[J]. 电子学报, 2017, 45(2): 353-358. doi: 10.3969/ j.issn.0372-2112.2017.02.013.

ZHANG Lifeng, LIU Zhaolin, and TIAN Pei. Image reconstruction algorithm for electrical capacitance tomography based on compressed sensing[J]. Acta Electronica Sinica, 2017, 45(2): 353-358. doi: 10.3969/j.issn. 0372-2112.2017.02.013.

DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/ TIT.2006.871582.

TSAIG Y and DONOHO D L. Extensions of compressed sensing[J]. Signal Processing, 2006, 86(3): 549-571. doi: 10.1016/j.sigpro.2005.05.029.

METZLER C A, MALEKI A, and BARANIUK R G. From denoising to compressed sensing[J]. IEEE Transactions on Information Theory, 2016, 62(9): 5117-5144. doi: 10.1109/ TIT.2016.2556683.

BIGOT J, BOYER C, and WEISS P. An analysis of block sampling strategies in compressed sensing[J]. IEEE Transactions on Information Theory, 2016, 62(4): 2125-2139. doi: 10.1109/TIT.2016.2524628.

ROMBERG J. Imaging via compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 14-20. doi: 10.1109/ MSP.2007.914729.

严韬, 陈建文, 鲍拯. 一种基于压缩感知的天波超视距雷达短时海杂波抑制方法[J]. 电子与信息学报, 2017, 39(4): 945-952. doi: 10.11999/JEIT160576.

YAN Tao, CHEN Jianwen, and BAO Zheng. Sea clutter suppression method for over-the-horizon radar with short coherent integration time based on compressed sensing [J]. Journal of Electronics Information Technology, 2017, 39(4): 945-952. doi: 10.11999/JEIT160576.

程银波, 司菁菁, 候肖兰. 适用于无线传感器网络的层次化分布式压缩感知[J]. 电子与信息学报, 2017, 39(3): 539-545. doi: 10.11999/JEIT160439.

CHENG Yinbo, SI Jingjing, and HOU Xiaolan. Hierarchical distributed compressed sensing for wireless sensor network [J]. Journal of Electronics Information Technology, 2017, 39(3): 539-545. doi: 10.11999/JEIT160439.

FRACASTORO G, FOSSON S M, and MAGLI E. Steerable discrete cosine transform[J]. IEEE Transactions on Image Processing, 2017, 26(1): 303-314. doi: 10.1109/TIP.2016. 2623489.

RAMAKRISHNAN A G, ABHIRAM B, and MAHADEVA P S R. Voice source characterization using pitch synchronous discrete cosine transform for speaker identification[J]. The Journal of the Acoustical Society of America, 2015, 137(6): EL469-EL475. doi: 10.1121/1.4921679.

MESSAOUDI A and SRAIRI K. Colour image compression algorithm based on the DCT transform using difference lookup table[J]. Electronics Letters, 2016, 52(20): 1685-1686. doi: 10.1049/el.2016.2115.

CANDES E J, ROMBERG J, and TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509. doi: 10.1109/TIT.2005.862083.

CANDES E J, ROMBERG J, and TAO T. Stable signal recovery from incomplete and inaccurate measurements[J]. Communications on Pure and Applied Mathematics, 2006, 59(8): 1207-1223. doi: 10.1002/cpa.20124.

CANDES E J and TAO T. Decoding by linear programming [J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215. doi: 10.1109/TIT.2005.858979.

NATARAJAN B K. Sparse approximate solutions to linear systems[J]. SIAM Journal on Computing, 1995, 24(2): 227-234. doi: 10.1137/S0097539792240406.

CHEN S S, DONOHO D L, and SAUNDERS M A. Atomic decomposition by basis pursuit[J]. SIAM Review, 2001, 43(1): 129-159. doi: 10.1137/S003614450037906X.

TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666. doi: 10.1109/TIT.2007.909108.

NEEDELL D and VERSHYNIN R. Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit[J]. Foundations of Computational Mathematics, 2009, 9(3): 317-334. doi: 10.1007/s10208-008- 9031-3.

THONG T D, LU Gan, NAM Nguyen, et al. Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]. 2008 42nd Asilomar Conference on Signals Systems and Computers, Pacific Grove, CA, USA, 2008, 10: 581-587.

吴新杰, 黄国兴, 王静文. 压缩感知在电容层析成像流型辨识中的应用[J]. 光学精密工程, 2013, 21(4): 1062-1068. doi: 10.3788/OPE.20132104.1062.

WU Xinjie, HUANG Guoxing, and WANG Jingwen. Application of compressed sensing to flow pattern identification of ECT[J]. Optics and Precision Engineering, 2013, 21(4): 1062-1068. doi: 10.3788/OPE.20132104.1062.

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