基于改进SR3模型算法的ECT图像重建研究

doi:10.3969/j.issn.1000-1158.2023.01.14

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Abstract In order to solve the problem of discomfort in image reconstruction, a sparse relaxation regularized regression model (SR3) was proposed for ECT image reconstruction when capacitance tomography was applied to gas-solid two-phase flow detection. Soft threshold iteration method and gradient descent method were used as solvers for SR3 model, L1 and L2 penalty terms were added to SR3 model, and filter element was designed to optimize solution vector. Experimental results show that compared with Tikhonov regularization algorithm, L1 regularization algorithm and the original algorithm of SR3 model, the algorithm of improved SR3 model has significantly improved the reconstructed image accuracy, significantly reduced the relative error of the image, and has better imaging effect.

Key words： metrology capacitance tomography SR3 model algorithm regularization algorithm of imaging reconstruction multiphase flow

Received: 09 March 2022 Published: 13 January 2023

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TB937

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	MA Min
	GUO Xin

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MA Min,GUO Xin. Research on ECT Image Reconstruction Based on Improved Algorithm of SR3 Model[J]. Acta Metrologica Sinica, 2023, 44(1): 95-102.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.01.14 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I1/95

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