A Trained Iterative Shrinkage Approach Based on Born Iterative Method for Electromagnetic Imaging

A trained-based Born iterative method (TBIM) is developed for electromagnetic imaging (EMI) applications. The proposed TBIM consists of a nested loop; the outer loop executes TBIM iteration steps, while the inner loop executes a trained iterative shrinkage thresholding algorithm (TISTA). The applied TISTA runs linear Landweber iterations implemented with a trained regularization network designed based on U-net architecture. A normalization process was imposed on the linear operator to make the linear inverse problem in TBIM consistent with TISTA assumptions. The iterative utilization of the regularization network in TISTA is a bottleneck that demands high memory allocation through the training process. Therefore, TISTA within each TBIM step was trained separately. The TISTA regularization network in each TBIM step was initialized using the weights from the previous TBIM step. The abovementioned approach achieved high-quality image restoration after running a few TBIM steps while maintaining low memory allocation through the training process. The numerical results illustrated show the superiority of the proposed TBIM compared with another three inverse scattering schemes. In addition, verification of experimental results based on the Fresnel institute database is demonstrated.