EAF-WGAN: Enhanced Alignment Fusion-Wasserstein Generative Adversarial Network for Turbulent Image Restoration
Xiangqing Liu, Gang Li, Zhenyang Zhao, Qi Cao, Zijun Zhang, Shaoan Yan, Jianbin Xie, Minghua Tang
Abstract
Because of optical distortion induced by atmospheric turbulence and the limitations of optical devices, acquired images of space objects are blurred and degraded. This effect results in anamorphosis in the output of two-dimensional imaging systems. In this work, we present a novel Enhanced Alignment Fusion-Wasserstein Generative Adversarial Network, called EAF-WGAN, for turbulent image restoration. This model is characterized by the innovative use of two modules, including the Align Module (AM) and the Feature Fusion Module (FFM) in the generator, especially in the process of feature fusion, in which 3D convolution is used. Through 3D convolution, the temporal and spatial information of the input image is obtained. Therefore, the formation mode and intensity of turbulence are not considered and the image can be reconstructed. The ability of EAF-WGAN is proved by algorithmically simulated data, physically simulated data, and real-world data.