Deep Unfolding Contrast Source Inversion for Strong Scatterers via Generative Adversarial Mechanism

To alleviate the extremely intrinsical ill-posedness and nonlinearity of electromagnetic inverse scattering under high contrast and low signal to noise ratio (SNR), we propose a deep unfolding network based on generative adversarial network (GAN) under contrast source inversion (CSI) framework, termed UCSI-GAN. The method solves inverse scattering problems (ISPs) using end-to-end generating confrontation way by incorporating a physical model together with its iterative updating formulation into the internal architecture of GAN. First, the nonlinear iterative scheme is extended to a deep unfolding generator network, and the contrast source and contrast updates are mapped to each module of the generator network. Second, to stabilize the imaging process, we add a refinement network to each variable update. Finally, the discriminator network is employed to ensure the authenticity of reconstructed images. The generator network and discriminator network are alternately trained with a generative adversarial learning strategy to reconstruct the properties of the medium object. Numerical experiments demonstrated that the performance of UCSI-GAN is better than traditional CSI and state-of-the-art learning approaches under high contrast and low SNR condition.