MIT Image Reconstruction Method Based on Simulated Annealing Particle Swarm Algorithm

doi:10.12068/j.issn.1005-3026.2021.04.011

Abstract

Abstract: In order to improve the ill-posed inverse problem and improve the quality of image reconstruction， a MIT image reconstruction method based on simulated annealing and particle swarm optimization was proposed. According to the dimensions of the Hessian matrix， a Tikhonov and NOSER hybrid multi-parameter regularization algorithm was constructed. The simulated annealing algorithm and particle swarm algorithm were combined， the objective function was constructed by the generalized cross criterion， and the regularized multi-parameter optimization was performed.The results show that not only the proposed method effectively overcomes the instability of the numerical solution of the MIT reconstructed image and enhances the anti-noise performance， but also the quality of the obtained reconstructed image is better than that of Tikhonov regularization and hybrid regularization algorithms， which provides a theoretical reference for the application of MIT technology.

Key words: ill-posed inverse problem; image reconstruction; Hessian matrix; simulated annealing; particle swarm optimization

CLC Number:

TP391.9

YANG Dan， LU Tian， GUO Wen-xin， WANG Xu. MIT Image Reconstruction Method Based on Simulated Annealing Particle Swarm Algorithm[J]. Journal of Northeastern University(Natural Science), 2021, 42(4): 531-537.

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