Electrical capacitance tomography image reconstruction based on iterative Tikhonov regularization improved algorithm
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Abstract:
Aiming at the problems of low reconstruction quality, poor robustness, and the inability to quickly and stably converge caused by the ill-posedness of electrical capacitance tomography image reconstruction, an improved algorithm based on iterative Tikhonov regularization was proposed. The algorithm constructs a new objective function by introducing the norm to carry out multi-criteria constraints, and introduces the result of the corrected Tikhonov regularization algorithm into the image reconstruction process together with the logarithmic weight factor as the estimated value. At the same time, an acceleration strategy is used, and the residual term is exponentially filtered. Perform ablation, initial value sensitivity, convergence, and noise interference experiments on the improved algorithm and compare it with other common algorithms. Experimental results show that the improved algorithm can quickly and stably converge and has good robustness and initial value insensitivity. The reconstructed image quality is high, the average correlation coefficient can reach 0.9633, and the average relative error can be reduced to 0.0694.
Keywords:
Project Supported:
The National Natural Science Foundation of China(Grant no. 61961037) and the Industrial Support Plan Project of Gansu Provincial Department of Education (Grant no. 2021CYZC-30).
Clc Number:
