Unsupervised adverse weather-degraded image restoration via contrastive learning

Abstract

Mitigating the impact of adverse weather on images, such as rain, haze, snow and raindrops, poses a critical challenge in numerous computer vision tasks, particularly in outdoor scenarios like port security and traffic surveillance. Recent successful methods for restoring images affected by severe weather have predominantly embraced supervised learning, which heavily depend on the quality of collected image pairs. However, capturing ideal image pairs for adverse weather image restoration in real-world scenarios is nearly impossible. In practice, unpaired images are more commonly available. The absence of proper supervision among unpaired images can result in low-quality image restoration outcomes. Therefore, utilizing unpaired image data for adverse weather image restoration remains a significant challenge. In this paper, we propose an effective method for Unsupervised Adverse weather-degraded Image Restoration (UAIR). Our approach leverages contrastive learning to explore both the similarities and differences in deep feature space among images. We not only utilize the intrinsic similarities between restored image and original degraded image to guide the content of the restored image, but also take advantage of the categoricaly differences within unpaired image data, thereby strengthening the connections between the restored image and the clean image at category level. Extensive experiments conducted on benchmark datasets for various tasks, including image snow removal, combined image rain and haze removal and image raindrop removal demonstrate that our proposed method achieves state-of-the-art performance on both weather-specific and all-in-one weather image restoration.