Abstract
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.
Abstract (translated)
虽然深度学习在单张图像去雨方面取得了进展，但现有的模型存在一个根本性的局限：它们使用静态的推理范式，无法适应现实世界中降雨带来的复杂、耦合退化（例如噪声痕迹、模糊和颜色偏差）。因此，恢复后的图像常常表现出残留的艺术品，并且感知质量不一致。在本工作中，我们提出了Derain-Agent，这是一个即插即用的优化框架，将去雨处理从静态处理转变为动态代理修复。Derain-Agent为基本的去雨模型配备了两大核心能力：1）一个规划网络，能够智能地为每个实例安排最优的恢复工具序列；2）一个强度调节机制，以空间自适应的方式应用这些工具。这种设计能够在不增加迭代搜索成本的前提下实现精确、区域特定的错误修正。我们的方法展示了强大的泛化能力，在合成和现实世界基准测试中持续提高了最先进的去雨模型的表现水平。
URL
https://arxiv.org/abs/2603.11866
