Application of Generative Adversarial Network-Based Inversion Algorithm in Imaging 2-D Lossy Biaxial Anisotropic Scatterer
Xiuzhu Ye, Naike Du, Daohan Yang, Xujin Yuan, Rencheng Song, Sheng Sun, Daining Fang
Abstract
An effective quasi-real-time inversion algorithm based on the super-resolution generative adversarial network (SR-GAN) is proposed to quantitatively image the 2-D biaxial anisotropic scatterers. The SR-GAN was originally proposed for the purpose of super-resolution image reconstruction, which exactly fits the need for inverse problem. In addition, Visual Geometry Group (VGG) loss is introduced to extract the high-level features of the object instead of the low-level pixel-wise error measures. The angle-dependent reconstruction effect due to the dipole radiation as in the traditional inversion methods is effectively resolved by the machine learning method. Numerical results using both synthetic data and experimental data are given to validate the effectiveness of the proposed method. Both the imaging quality and resolution are greatly improved by the proposed SR-GAN algorithm, compared to the traditional iterative inversion algorithm. In addition, the computational time is reduced significantly and the quasi-real-time imaging is finally realized, which promises a potential real-time application of the inverse scattering method.