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.
@article{arxiv.2603.11866,
title = {Derain-Agent: A Plug-and-Play Agent Framework for Rainy Image Restoration},
author = {Zhaocheng Yu and Xiang Chen and Runzhe Li and Zihan Geng and Guanglu Sun and Haipeng Li and Kui Jiang},
journal= {arXiv preprint arXiv:2603.11866},
year = {2026}
}