Unifying Physically-Informed Weather Priors in a Single Model for Image Restoration Across Multiple Adverse Weather Conditions

Image restoration under multiple adverse weather conditions aims to develop a single model to recover the underlying scene with high visibility. Weather-related artifacts vary with the particle's distance to the camera according to the established scene visibility analysis, where close and faraway regions are more affected by falling drops and fog effects, respectively. In challenging weather conditions, existing image restoration methods fall short by not accounting for the varying impact of adverse weather on different scene regions. We develop a novel unified imaging model combined with a weather-prior-based network that directly incorporates weather-specific physical imaging processes into the restoration process. This approach not only enhances visibility in both near and distant regions affected by drops but also outperforms current state-of-the-art methods by effectively mitigating artifacts such as fog. Our contributions include a comprehensive analysis of weather-related visual factors and the development of an innovative network architecture that leverages estimated occlusion and transmission to restore scene details. Experimental results on three synthetic benchmarks, including our Weather30K dataset, along with two all-weather datasets, and a real-world benchmark with challenging mixed weather conditions, show the superiority of our method against state-of-the-art methods.