Electromagnetic Imaging of Metal Defects Based on Bayesian Statistical Model

Traditional regularization algorithms for electromagnetic tomography (EMT) reconstruction merely obtains the approximate estimation value of a single conductivity, and the model information provided is limited. A large number of reasonable model parameter estimates are obtained by statistical methods. In this paper, we propose an EMT image reconstruction method based on Bayesian statistical model. According to the sparsity of defect distribution, the solved conductivity can be divided into a series of block structures. With the aid of sparse Bayesian learning (SBL) framework, statistical information, including the prior probability of the conductivity sparse distribution and the noise information in the measurement data, is taken into account. Hence the full statistical description of the conductivity distribution can be obtained. Both simulation and experimental results show that this method effectively improves the quality and accuracy of the defect image.