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

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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

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	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.

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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

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