Physical model-driven deep networks for through-the-wall radar imaging
Yuhao Wang, Yue Zhang, Mingcheng Xiao, Huilin Zhou, Qiegen Liu, Jianfei Gao
In order to merge the advantages of the traditional compressed sensing (CS) methodology and the data-driven deep network scheme, this paper proposes a physical model-driven deep network, termed CS-Net, for solving target image reconstruction problems in through-the-wall radar imaging. The proposed method consists of two consequent steps. First, a learned convolutional neural network prior is intro