Abstract:This paper proposes an image reconstruction algorithm based on Bayesian theory for electromagnetic tomography (EMT). The traditional regularization algorithms for EMT reconstruction can only achieve a single estimation. Hence, the information provided by the model is limited. A large number of reasonable parameter estimations for the model can be obtained by statistical methods. According to the sparsity of defect distribution, the conductivity distribution is divided into a series of block structures. Under the framework of sparse Bayesian learning, statistical information, including the prior information of sparse representation for conductivity distribution and the noise information in the measurement data, is taken into account. In this way, the full statistical description of the conductivity distribution can be obtained. The conductivity distribution for the surface defects of metal part is reconstructed based on the sparse Bayesian algorithm. To further prove the feasibility of this algorithm, the reconstruction results of the new method is compared with those of the conjugate gradient method and the total variation regularization method. The defect imaging experiments are implemented based on the EMT system. Compared with traditional methods, both simulation and experimental results show that the relative errors of reconstructed images based on the Bayesian algorithm with statistical information can be reduced by 20%. The quality and accuracy of defects images are effectively improved.