Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start
Fuyang Liu, Jiaqi Xu, Xiaowei Hu
Adverse weather significantly degrades real-world visual perception, posing a challenge to the robustness of vision systems. Existing methods, often trained on synthetic data with static network parameters, struggle to generalize to complex real-world degradations. To address this, we develop a dual-level reinforcement learning framework that leverages high-quality synthetic data as a cold start and adaptively refines the models using real-world data through reinforcement learning. First, we construct HFLS-Weather, a physics-driven, high-fidelity synthetic dataset that simulates diverse weather phenomena. Second, we formulate a dual-level reinforcement learning architecture: at the local level, weather-specific restoration models are refined using the proposed perturbation-driven image quality optimization, enabling reward-based learning without paired supervision for image restoration; at the global level, a meta-controller dynamically coordinates model selection and execution order based on scene degradation. Our framework continuously adapts to real-world conditions and achieves state-of-the-art performance across a wide range of real-world adverse weather scenarios.