Restoration of Single Sand-Dust Image Based on Style Transformation and Unsupervised Adversarial Learning

Abstract

Since dust particles in the air scatter and absorb light, images captured in sand-dust weather mostly show low contrast, color deviation and blurriness, seriously affecting the reliability of visual tasks. Currently, pixel-level enhancement and prior-based methods are used to restore sand-dust images. However, these methods cannot accurately extract semantic information from the images due to the loss of information and the complexity of the scene depth, which may lead to color distortion and blurred textures of the restored image. We thus presents a two-stage restoration method based on style transformation and unsupervised sand-dust image restoration network (USDR-Net). In the first stage, the grayscale distribution compensation (GDC) method is used to transform the style of the sand-dust image. After transformation, color shift is eliminated and potential information is restored in the balanced image. In the second stage, USDR-Net firstly employs the dark channel prior and the transmission map enhancement network (TME-Network) to generate and refine the transmission map of the balanced image to improve the accuracy of scene depth. Then, it reconstructs a clear image with actual color and high contrast via adversarial learning with unpaired sand-dust and clear images. Extensive experimental results show that our method outperforms state-of-the-art algorithms based on both qualitative and quantitative evaluations. The mean average precision for the target inspection datasets has increased from 16.79% to 68.82%.