Abstract
Natural sand-dust weather is complicated, and synthetic sand-dust datasets cannot accurately reflect the properties of real sand-dust images. Sand-dust image enhancement and restoration methods that are based on enhancement, on priors, or on data-driven may not perform well in some scenes. Therefore, it is important to develop a robust sand-dust image restoration method to improve the information processing ability of computer vision. In this paper, we propose a new zero-shot learning method based on an atmospheric scattering physics model to restore sand-dust images. The technique has two advantages: First, as it is unsupervised, the model can be trained without any prior knowledge or image pairs. Second, the method obtains transmission and atmospheric light by learning and inferring from a single real sand-dust image. Extensive experiments are performed and evaluated both qualitatively and quantitatively. The results show that the proposed method works better than the state-of-the-art algorithms for enhancing and restoring sand-dust images.