Self-Supervised Learning of Physics-Guided Deep Unfolding Parallel Networks for Solving Nonlinear Inverse Scattering Problems

Numerous learning-based methods have been proposed to address the challenging task of solving inverse scattering problems (ISPs), with the requirement of ample labeled training datasets. To overcome the difficulty of collecting fully labeled datasets, we present a physically guided deep unfolding parallel network with self-supervised learning strategy to solve nonlinear ISPs, called SSISP-Net. The proposed method incorporates a physical model and the iterative optimization algorithm of ISPs into the network architecture and, thus, forms a deep unfolding network. First, here, two subsets of scattered fields are generated from fully measured scattered field data using receiver antenna positions. Second, we construct a parallel network by using two deep unfolding networks and then train this parallel network using only these two subsets of the scattered field. Finally, to constrain the two unfolding networks in the parallel network, we design two scattered field crossing losses and a contrast difference loss in the network. In this way, the reconstructed model can be properly trained using only scattered field data. The numerical and experimental results demonstrate that the proposed self-supervised learning method outperforms traditional and several other supervised learning methods under similar conditions. Moreover, it achieves competitive reconstruction performance compared with supervised learning methods known for their superior reconstruction capabilities.