用真实天气数据训练模型，让图像修复自动适应复杂恶劣环境。
Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start
- 分层强化学习：局部优化特定天气修复，全局动态调度模型顺序。
- 在多种恶劣天气下超越现有方法，实现端到端自适应修复。
- 基于物理模拟数据冷启动，无需成对标注即可学习质量奖励。
恶劣天气严重损害真实场景视觉感知，而现有基于合成数据、参数固定的视觉模型难以泛化至复杂退化场景。为此，我们首先构建了基于物理驱动的高保真数据集HFLS-Weather，模拟多样天气现象；随后设计一种以HFLS-Weather为冷启动初始化的双层强化学习框架。在局部层面，通过扰动驱动的图像质量优化，对特定天气修复模型进行微调，实现无配对监督的奖励学习；在全局层面，元控制器根据场景退化程度动态选择并调度模型执行顺序。该框架支持对真实环境的持续适应，在广泛恶劣天气场景中达到当前最优性能。代码已公开于https://github.com/xxclfy/AgentRL-Real-Weather。
原文摘要 · Abstract (English)
Adverse weather severely impairs real-world visual perception, while existing vision models trained on synthetic data with fixed parameters struggle to generalize to complex degradations. To address this, we first construct HFLS-Weather, a physics-driven, high-fidelity dataset that simulates diverse weather phenomena, and then design a dual-level reinforcement learning framework initialized with HFLS-Weather for cold-start training. Within this framework, at the local level, weather-specific restoration models are refined through perturbation-driven image quality optimization, enabling reward-based learning without paired supervision; at the global level, a meta-controller dynamically orchestrates model selection and execution order according to scene degradation. This framework enables continuous adaptation to real-world conditions and achieves state-of-the-art performance across a wide range of adverse weather scenarios. Code is available at https://github.com/xxclfy/AgentRL-Real-Weather
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