用智能代理动态修复雨天图像，效果更准更自然。
Derain-Agent: A Plug-and-Play Agent Framework for Rainy Image Restoration
- 引入规划网络和强度调节机制，实现按需自适应修复
- 在真实雨天数据上提升主流模型性能，残余污点减少
- 即插即用，适合想快速提升现有去雨模型的开发者
尽管深度学习已推动单图去雨技术发展，但现有模型受限于静态推理范式，难以应对真实场景中噪声、模糊和色偏等复杂耦合退化问题，导致复原图像常残留伪影且感知质量不一。本文提出 Derain-Agent，一种即插即用的动态修复框架，将去雨从静态处理转变为基于智能体的动态修复。该框架为基线去雨模型赋予两项核心能力：1）规划网络可为每张图像智能调度最优的修复工具序列；2）强度调节机制实现空间自适应的工具应用强度。该设计可在不进行高成本迭代搜索的前提下，精准定位并修正局部残余误差。实验表明，该方法具备强泛化能力，在合成与真实世界基准上均持续提升当前先进去雨模型的表现。
原文摘要 · Abstract (English)
While deep learning has advanced single-image deraining, existing models suffer from a fundamental limitation: they employ a static inference paradigm that fails to adapt to the complex, coupled degradations (e.g., noise artifacts, blur, and color deviation) of real-world rain. Consequently, restored images often exhibit residual artifacts and inconsistent perceptual quality. In this work, we present Derain-Agent, a plug-and-play refinement framework that transitions deraining from static processing to dynamic, agent-based restoration. Derain-Agent equips a base deraining model with two core capabilities: 1) a Planning Network that intelligently schedules an optimal sequence of restoration tools for each instance, and 2) a Strength Modulation mechanism that applies these tools with spatially adaptive intensity. This design enables precise, region-specific correction of residual errors without the prohibitive cost of iterative search. Our method demonstrates strong generalization, consistently boosting the performance of state-of-the-art deraining models on both synthetic and real-world benchmarks.
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