  Electromagnetic inverse scattering problems (ISPs) aim to retrieve
permittivities of dielectric scatterers from the scattering measurement. It is
often highly nonlinear, caus-ing the problem to be very difficult to solve. To
alleviate the issue, this letter exploits a linear model-based network (LMN)
learning strategy, which benefits from both model complexity and data learning.
By introducing a linear model for ISPs, a new model with network-driven
regular-izer is proposed. For attaining efficient end-to-end learning, the
network architecture and hyper-parameter estimation are presented. Experimental
results validate its superiority to some state-of-the-arts.
