Research on electrical impedance imaging based on regularization parameter optimization and boundary clustering
Electrical impedance tomography(EIT)is a non-destructive functional imaging technique,due to the inherent characteristics of the inverse problem,such as discomfort and instability,there are often problems such as low resolution and large artifacts of reconstructed images.In this paper,the penalty functions of two regularization algorithms Tikhonov and TV are combined,and particle swarm optimization is proposed to optimize the regularization parameters of the combined penalty function.The image quality indicator AL is taken as the fitness value of the particle swarm algorithm,so as to determine the optimal parameters,and the conductivity is obtained through iterative solution by Newton iteration method.In order to further remove artifacts,Niblack algorithm and boundary clustering algorithm are combined to process the obtained conductivity,and the final conductivity distribution is obtained.The results of simulation and measurement show that the reconstructed image can reflect the position information of the target object more accurately,effectively inhibit the generation of artifacts,and improve the reconstruction effect.
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