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arXiv · 2603.11866

Derain-Agent: A Plug-and-Play Agent Framework for Rainy Image Restoration

Zhaocheng Yu·Xiang Chen·Runzhe Li·Zihan Geng·Guanglu Sun·Haipeng Li·Kui Jiang
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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.

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Zhaocheng Yu, Xiang Chen, Runzhe Li, Zihan Geng, Guanglu Sun, Haipeng Li, Kui Jiang. 2026-03-12. Derain-Agent: A Plug-and-Play Agent Framework for Rainy Image Restoration. https://arxiv.org/abs/2603.11866

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