联合改进稀疏正则化ECT图像重建算法

doi:10.3969/j.issn.1000-1158.2024.08.07

Abstract
Figure/Table
References (18)
Related Citation (15)

Download: PDF (0 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract To improve the ill-conditioned and ill-posed problem in the inverse problem solving process of electrical capacitance tomography (ECT), a jointly improved sparse regularization image reconstruction algorithm is proposed. Firstly, the sensitivity matrix is optimally preprocessed by the adaptive truncated singular value algorithm to eliminate the redundant information in the matrix. Secondly, in order to enhance the sparsity and stability of the solution, the L1-αL2 sparse regularization is jointly improved based on the optimized sensitivity matrix to construct new convex function terms. Finally, the solution is performed by the fast iterative threshold shrinkage algorithm to accelerate the iterative convergence speed. The improved algorithm achieves an average correlation coefficient of 0.8813 in the reconstructed image, the image error is reduced to 0.2111 on average, and the imaging speed is kept within 0.10s. The simulation and experimental results show that the improved algorithm improves the ill-posed and ill-condition degree and enhances the image reconstruction accuracy while having strong robustness and real-time performance.

Key words： multiphase flow measurement electrical capacitance tomography adaptive truncated singular value sparse regularization image reconstruction

Received: 29 October 2023 Published: 05 September 2024

PACS:	TB937
	TB973

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	MA Min
	SUN Ni

Cite this article:

MA Min,SUN Ni. Jointly Improved Sparse Regularization ECT Image Reconstruction Algorithm[J]. Acta Metrologica Sinica, 2024, 45(8): 1132-1138.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.08.07 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I8/1132

［2］	马敏, 于洁. 基于改进联合稀疏电容层析成像算法滑油监测研究［J］. 推进技术, 2022, 43(4): 345-351.
［8］	GAO Y L, XU Q, LUO K M, et al. Overview on Image Reconstruction Algorithms for Electrical Capacitance Tomography［C］//37th Chinese Control Conference (CCC). 2018.
［4］	MERIBOUT M, SAIED I M. Real-Time Two-Dimensional Imaging of Solid Contaminants in Gas Pipelines Using an Electrical Capacitance Tomography System［J］. IEEE Transactions on Industrial Electronics, 2017, 64(5): 3989-3996.
［6］	WANG C, GUO Q, WANG H X, et al. ECT Image Reconstruction Based on Alternating Direction Approximate Newton Algorithm［J］. IEEE Transactions on Instrumentation and Measurement, 2020, 69(7): 4873-4886.
［12］	GUO H B, LIU S, GUO H Q. Hybrid Iterative Reconstruction Method for Imaging Problems in ECT［J］. IEEE Transactions on Instrumentation and Measurement, 2020, 69(10): 8238-8249.
［14］	JIAO Y L, JIN Q N, LU X L, et al. Alternating direction method of multipliers for linear inverse problems［J］. SIAM Journal on Numerical Analysis, 2016, 54(4): 2114-2137.
［1］	王化祥. 电学层析成像技术［J］. 自动化仪表, 2017, 38(5): 1-6.
	WANG H X. Electrical Tomography Technology ［J］. Process Automation Instrumentation, 2017, 38(5): 1-6.
	MA M, YU J. Lubricating Oil Monitoring Based on Improved Joint Sparse Electrical Capacitance Tomography Algorithm ［J］. Journal of Propulsion Technology, 2022, 43(4): 345-351.
［5］	ZHANG J L, MAO M X, YE J M, et al. Investigation of Wetting and Drying Process in a Gas-Solid Fluidized Bed by Electrical Capacitance Tomography and Pressure Measurement［J］. Powder Technology, 2016, 301(1): 1148-1158.
［7］	CUI L Q, TIAN P, WANG C, et al. Iterative weighted fidelity and hybrid regularization-based algorithm for ECT［J］. Measurement Science and Technology, 2022, 33(6): 065404.
［9］	马敏, 刘一斐, 刘亚楠. 基于改进半阈值迭代算法的ECT图像重建［J］. 计量学报, 2021, 42(5): 595-602.
	MA M, LIU Y F, LIU Y N. ECT lmage Reconstruction Based on lmproved Half-threshold lterative Algorithm ［J］. Acta Metrologica Sinica, 2021, 42(5): 595-602.
［10］	马敏, 郭鑫. 基于改进SR3模型算法的ECT图像重建研究［J］. 计量学报, 2023, 44(1): 95-102.
	MA M, GUO X. Research on ECT lmage Reconstruction Based on lmproved Algorithm of SR3 Model ［J］. Acta Metrologica Sinica, 2023, 44(1): 95-102.
［13］	TAO P D, AN L T H. A DC optimization algorithm for solving the trust-region subproblem［J］. SIAM Journal on Optimization, 1998, 8(2): 476-505.
［15］	WEN B, CHEN X J, PONG T K. A proximal difference-of-convex algorithm with extrapolation［J］. Computational Optimization and Applications, 2018, 69(2): 297-324.
［17］	BECK A, TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems［J］. SIAM Journal Imaging Sciences, 2009, 2(1): 183-202.
［18］	HUANG G X, SUN J T, LU W D, et al. ECT Image Reconstruction Method Based on Multi-Exponential Feature Extraction［J］. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-14.
［3］	QIN X B, JI C C, SHEN Y T, et al. ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling［J］. Advances in Civil Engineering, 2021, 2021: 6677639.
［11］	LI S X, WANG H X, CHEN J. N, et al. An Improved Sparse Reconstruction Algorithm Based on Singular Value Decomposition for Electrical Resistance Tomography［C］//2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). 2021.
［16］	LOU Y F, YIN P H, HE Q, et al. Computing Sparse Representation in a Highly Coherent Dictionary Based on Difference of L1 and L2［J］. Journal of Scientific Computing, 2015, 64(1): 178-196.