An inhomogeneous background imaging method based on generative adversarial network

A deep learning-based inversion algorithm is developed to solve the inhomogeneous background inverse scattering problem (ISP). To alleviate the burden of nonlinearity and ill-posedness of the ISP, a noniterative method called the distorted-Born backpropagation scheme is introduced to quantitatively reconstruct a rough image of the unknown object in inhomogeneous background. The roughly reconstructed result serves as the input of the designed generative adversarial network (GAN), which outputs the fine reconstructed image of the relative permittivity. The generator network of the GAN is well designed as an encoder-decoder structure configured with the attention scheme. The discriminator network is taken to supervise the generator to learn the features of target scatterers through an adversarial training process. The proposed method is proven to be effective in reconstructing objects embedded in inhomogeneous background, which promises a real-time application future of the ISP.