基于改进型迭代NR的磁感应断层成像图像重建算法

doi:10.3969/j.issn.0258-8021.2015. 02.009

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Abstract The image reconstruction process is a typical illposed problem in magnetic induction tomography (MIT), in which the numerical solution is unstable. To solve this problem, an improved iteration NewtonRaphson algorithm based on weighted matrix and L1norm regularization is improved. The proposed method adds the weight matrix in the objective function and adds L1norm regularization term in L2norm regularization penalty term. The analysis is made for three typical models in the data with and without noise, respectively. And the proposed algorithm is contrasted with Tikhonov regularization algorithm and iterative NR algorithm. In the data without noise, relative to Tikhonov regularization algorithm and iterative NR algorithm, the relative error is reduced by 011-014. And then, the correlation coefficient is raised by 13%-17%. The algorithm has good performance in imaging. In the data with noise, the relative error is reduced by 006-009, and the correlation coefficient is raised by 7%-10% in the proposed algorithm. The algorithm has good antinoise performance, which has offered theory basis for the study of reconstruction accuracy.

Key words： magnetic induction tomography (MIT) image reconstruction L1norm regularization iteration NewtonRaphson

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	Han Min^1*Xue Yuyan¹Qin Pan¹Han Jie

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Han Min^1*Xue Yuyan¹Qin Pan¹Han Jie. An Improved Image Reconstruction Algorithm Based on Iteration NR in Magnetic Induction Tomography[J]. journal1, 2015, 34(2): 190-197.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2015. 02.009 OR http://cjbme.csbme.org/EN/Y2015/V34/I2/190

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