Recent advances in image deraining have focused on training powerful models on mixed multiple datasets comprising diverse rain types and backgrounds. However, this approach tends to overlook the inherent differences among rainy images, leading to suboptimal results. To overcome this limitation, we focus on addressing various rainy images by delving into meaningful representations that encapsulate both the rain and background components. Leveraging these representations as instructive guidance, we put forth a Context-based Instance-level Modulation (CoI-M) mechanism adept at efficiently modulating CNN- or Transformer-based models. Furthermore, we devise a rain-/detail-aware contrastive learning strategy to help extract joint rain-/detail-aware representations. By integrating CoI-M with the rain-/detail-aware Contrastive learning, we develop CoIC, an innovative and potent algorithm tailored for training models on mixed datasets. Moreover, CoIC offers insight into modeling relationships of datasets, quantitatively assessing the impact of rain and details on restoration, and unveiling distinct behaviors of models given diverse inputs. Extensive experiments validate the efficacy of CoIC in boosting the deraining ability of CNN and Transformer models. CoIC also enhances the deraining prowess remarkably when real-world dataset is included.

翻译：近年来，图像去雨领域的研究重点在于利用包含多种雨型与背景的混合多数据集训练强大模型。然而，这种方法往往忽视了雨天图像固有的差异性，导致结果次优。为突破此局限，我们聚焦于通过挖掘能同时编码雨纹与背景成分的有意义表示来处理多样化的雨天图像。基于这些表示作为指导性线索，我们提出了一种基于上下文的实例级调制机制（CoI-M），该机制能高效调控基于CNN或Transformer的模型。此外，我们设计了一种雨纹/细节感知对比学习策略，以辅助提取联合雨纹/细节感知表示。通过将CoI-M与雨纹/细节感知对比学习相融合，我们开发了CoIC——一种专为混合数据集训练而设计的创新高效算法。进一步地，CoIC能提供关于数据集关系建模的洞察，定量评估雨纹与细节对复原的影响，并揭示模型对不同输入的不同行为。大量实验验证了CoIC在提升CNN和Transformer模型去雨能力方面的有效性。当引入真实世界数据集时，CoIC同样显著增强了去雨性能。