DELNet通过动态专家库实现天气图像修复的持续学习，无需重训练。
DELNet: Continuous All-in-One Weather Removal via Dynamic Expert Library
- 基于任务相似性判断和动态专家库，自动选择或添加修复专家。
- 在OTS、Rain100H、Snow100K上分别提升PSNR 16%、11%、12%。
- 适合需要长期更新修复能力的实际场景，如智能监控与自动驾驶。
一体化天气图像修复方法虽具实用价值，但依赖预收集数据，面对未见退化需重新训练，成本高。本文提出DELNet，一种面向天气图像修复的持续学习框架。DELNet集成判别阀以衡量任务相似性，区分新旧任务；并构建动态专家库，存储针对不同退化训练的专家模型。对于新任务，阀值选择前k个专家进行知识迁移，并加入新专家以捕捉特定特征；对于已知任务，则直接复用对应专家。该设计实现无需重训练的持续优化。在OTS、Rain100H和Snow100K数据集上的实验表明，DELNet超越现有持续学习方法，分别获得16%、11%、12%的PSNR提升。结果验证了DELNet的有效性、鲁棒性和效率，显著降低重训练成本，支持真实场景中的实际部署。
原文摘要 · Abstract (English)
All-in-one weather image restoration methods are valuable in practice but depend on pre-collected data and require retraining for unseen degradations, leading to high cost. We propose DELNet, a continual learning framework for weather image restoration. DELNet integrates a judging valve that measures task similarity to distinguish new from known tasks, and a dynamic expert library that stores experts trained on different degradations. For new tasks, the valve selects top-k experts for knowledge transfer while adding new experts to capture task-specific features; for known tasks, the corresponding experts are directly reused. This design enables continuous optimization without retraining existing models. Experiments on OTS, Rain100H, and Snow100K demonstrate that DELNet surpasses state-of-the-art continual learning methods, achieving PSNR gains of 16\%, 11\%, and 12\%, respectively. These results highlight the effectiveness, robustness, and efficiency of DELNet, which reduces retraining cost and enables practical deployment in real-world scenarios.
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