Image deraining plays a pivotal role in low-level computer vision, serving as a prerequisite for robust outdoor surveillance and autonomous driving systems. While deep learning paradigms have achieved remarkable success in firmly aligned settings, they often suffer from severe performance degradation when generalized to unseen Out-of-Distribution (OOD) scenarios. This failure stems primarily from the significant domain discrepancy between synthetic training datasets and the complex physical dynamics of real-world rain. To address these challenges, this paper proposes a pioneering cross-scenario deraining adaptation framework. Diverging from conventional approaches, our method obviates the requirements for paired rainy observations in the target domain, leveraging exclusively rain-free background images. We design a Superpixel Generation (Sup-Gen) module to extract stable structural priors from the source domain using Simple Linear Iterative Clustering. Subsequently, a Resolution-adaptive Fusion strategy is introduced to align these source structures with target backgrounds through texture similarity, ensuring the synthesis of diverse and realistic pseudo-data. Finally, we implement a pseudo-label re-Synthesize mechanism that employs multi-stage noise generation to simulate realistic rain streaks. This framework functions as a versatile plug-and-play module capable of seamless integration into arbitrary deraining architectures. Extensive experiments on state-of-the-art models demonstrate that our approach yields remarkable PSNR gains of up to 32% to 59% in OOD domains while significantly accelerating training convergence.

翻译：图像去雨在低级计算机视觉中扮演着关键角色，是实现鲁棒户外监控和自动驾驶系统的基础前提。尽管深度学习范式在严格对齐的设置中取得了显著成功，但在推广到未见过的分布外（OOD）场景时，常遭受严重的性能下降。这一失败主要源于合成训练数据集与真实世界降雨复杂物理动力学之间的显著领域差异。为应对这些挑战，本文提出了一种开创性的跨场景去雨自适应框架。与传统方法不同，我们的方法规避了对目标领域配对雨观测的需求，仅利用无雨背景图像。我们设计了一个超像素生成模块，通过简单线性迭代聚类从源领域提取稳定的结构先验。随后，引入了一种分辨率自适应融合策略，通过纹理相似性将这些源结构与目标背景对齐，确保合成多样且真实的伪数据。最后，我们实现了伪标签重合成机制，采用多阶段噪声生成来模拟真实雨条纹。该框架作为一种通用的即插即用模块，可无缝集成到任意去雨架构中。在最新模型上的大量实验表明，我们的方法在OOD领域中取得了高达32%至59%的峰值信噪比（PSNR）增益，同时显著加速了训练收敛。