Abstract
U-Net with the iterative modified contrast scheme (IMCS) is proposed to solve inverse scattering problems (ISPs) in half-space. IMCS is an innovative inversion technique that utilizes contrast functions to improve the visibility of target regions and reconstruct the internal structure of objects. In contrast to applying IMCS alone, our proposed method improves the detection of contrast boundaries, enhancing noise immunity as well as increasing the structural similarity (SSI) through deep learning with U-Net. We compare the numerical results for 200-iteration IMCS and U-Net with 3-iteration IMCS, and it is found that the accuracy of reconstructed images can be improved a lot by U-Net with the 3-iteration IMCS architecture. In addition, even in the case of large Gaussian noise, the reconstruction is still good with our proposed method.