UnfairGAN: An enhanced generative adversarial network for raindrop removal from a single image

Abstract

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. UnfairGAN can effectively conserve the essential details caused by heavy raindrops and remove artifacts caused by the instability of training the discriminator. This method is based on three main primary advantages. First, UnfairGAN consists of an advanced loss function of the discriminator that can improve the instabilities of traditional GAN. Second, UnfairGAN comprises a new advanced activation function that is able to increase the learning effectiveness of image classification and reconstruction. Finally, UnfairGAN is basically built on a new end-to-end cascade network, namely DRD-UNet, used to probe hierarchical features for image restoration effectively. When evaluating competing methods on the well-known Raindrop dataset, we achieve a peak signal-to-noise ratio value of 31.56 while retaining the essential details in the image. Besides, we introduce a new large image dataset (DeepRaindrops) for training deep learning networks of removing raindrops. In this dataset, our proposed method is superior to other state-of-the-art approaches of deraining raindrops regarding quantitative metrics and visual quality. Our source codes for UnfairGAN are available at https://github.com/ZeroZero19/UnfairGAN.git. © 2022