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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.05095 |
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| _version_ | 1866914142463459328 |
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| author | Liu, Fuyang Xu, Jiaqi Hu, Xiaowei |
| author_facet | Liu, Fuyang Xu, Jiaqi Hu, Xiaowei |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05095 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start Liu, Fuyang Xu, Jiaqi Hu, Xiaowei Computer Vision and Pattern Recognition 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 |
| title | Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.05095 |