A sparse representation defect imaging method based on eddy current tomography

Abstract As an advanced non-destructive testing technique, eddy current tomography is suitable for defect detection in metallic samples due to the advantages of high sensitivity, fast response speed and intuitive detection results. However, the number of parameters to be solved for in eddy-current tomography is much larger than the number of measured data, which leads to the problem of under characterization of the inversion process. In this paper, a sparse representation reconstruction algorithm based on sigmoid radial basis functions is investigated. Specifically, the basis function vectors of discrete grid points in the imaging region are used to form a basis function matrix. An iterative algorithm is used to solve the fitting coefficients, which can approximate the electromagnetic properties of each point. In turn, the electromagnetic parameter distribution can be reconstructed, enabling the achievement of defect imaging. The method effectively improves the local area fitting accuracy and simplifies the complexity of the imaging model. Experiments are conducted to evaluate the performance of the proposed method using different defect distribution. Compared to the classical reconstruction method, the results indicate that the average relative error and correlation coefficient are improved by 10.8% and 36.2%, respectively.