  Image deraining is a new challenging problem in real-world applications, such
as autonomous vehicles. In a bad weather condition of heavy rainfall,
raindrops, mainly hitting glasses or windshields, can significantly reduce
observation ability. Moreover, raindrops spreading over the glass can yield
refraction's physical effect, which seriously impedes the sightline or
undermine machine learning systems. In this paper, we propose an enhanced
generative adversarial network to deal with the challenging problems of
raindrops. UnfairGAN is an enhanced generative adversarial network that can
utilize prior high-level information, such as edges and rain estimation, to
boost deraining performance. To demonstrate UnfairGAN, we introduce a large
dataset for training deep learning models of rain removal. The experimental
results show that our proposed method is superior to other state-of-the-art
approaches of deraining raindrops regarding quantitative metrics and visual
quality.
