Physical Model-Inspired Deep Unrolling Network for Solving Nonlinear Inverse Scattering Problems
Jian Liu, Huilin Zhou, Tao Ouyang, Qiegen Liu, Yuhao Wang
Abstract
In this article, to bridge the gap between the traditional model-based methods and data-driven deep learning schemes, we propose a physical model-inspired deep unrolling network for solving nonlinear inverse scattering problems, termed PM-Net. The proposed end-to-end network is formed by two consequent steps. First, an augmented Lagrangian method is introduced to transform a constrained objective function to be an unconstrained optimization. In addition, it is further decomposed into four quasi-linear subproblems. Second, we unfold the iterative scheme into a layer-wise deep neural network. Each subproblem is mapped into a module of the deep unrolling network. In PM-Net, these variables including the weight, the regularization of contrast, and other parameters are learned and updated alternately by corresponding network layers. PM-Net effectively combines neural networks with the knowledge of underlying physics as well as traditional techniques. Unlike existing networks, PM-Net explicitly exploits contrast source and contrast modules. Compared to traditional iterative methods, the performance of PM-Net is comparable or even better than subspace-based optimization method in the high noise-level circumstance. Compared to the state-of-the-art learning approaches, not only less network parameters need to be learned, but also better performance is achieved by PM-Net.