Multiple-Space Deep Learning Schemes for Inverse Scattering Problems

Recently, deep learning methods have made significant success in inverse scattering problems (ISPs). However, learning approaches that work in different spaces, such as frequency and real space, are seldom explored in solving ISPs. In this work, multiple-space deep learning schemes (MSDLSs) incorporating frequency-space and real-space processing are studied. Specifically, a network that works in low-frequency subspace is first introduced. Then, serial MSDLSs are introduced by combining frequency-space and real-space networks in a serial way to enable networks in different spaces work complementarily. Finally, to further enable dynamic interaction between multiple-space information during both training and testing stages, a parallel MSDLS is proposed. The proposed MSDLSs are presented under the framework of backpropagation scheme (BPS). It is shown by synthetic and experimental tests that the MSDLSs have a consistent improvement over BPS. It is expected that the proposed schemes will find applications on other inverse problems where an incorporation of multiple-space information is needed.