Image Reconstruction Based on Sequential Monte Carlo Principle for EMT
Guoxing Huang, Wenqing Qian, Jingwen Wang, Weidang Lu, Hong Peng
Abstract
Due to the single and limited prior information in electromagnetic tomography (EMT), the existing reconstruction methods are often unable to reconstruct the image with high accuracy for different kinds of flow patterns. In this article, an image reconstruction algorithm based on the sequential Monte Carlo principle for EMT is proposed. It can increase the diversity of samples to extract more image features for EMT images reconstruction and maintain a high image reconstruction effect for different flow patterns. First, the EMT image reconstruction process is modeled as a dynamic time-varying system to search for the optimal estimation in state space. At the same time, controllable adjustment of probability density distribution can improve the reconstruction effect under different flow patterns. Second, samples are obtained from the initial sample space, and their weights are updated according to the samples’ information. The numbers of effective samples and the diversity of samples are increased by resampling. Finally, the system state estimation is calculated according to the samples and their updated weights, and the system state is updated iteratively until the optimal solution is obtained. Simulation results have shown that the proposed method is superior to other existing methods in image error and correlation coefficient, and has better imaging quality.