The intricacy of rainy image contents often leads cutting-edge deraining models to image degradation including remnant rain, wrongly-removed details, and distorted appearance. Such degradation is further exacerbated when applying the models trained on synthetic data to real-world rainy images. We observe two types of domain gaps between synthetic and real-world rainy images: one exists in rain streak patterns; the other is the pixel-level appearance of rain-free images. To bridge the two domain gaps, we propose a semi-supervised detail-recovery image deraining network (Semi-DRDNet) with dual sample-augmented contrastive learning. Semi-DRDNet consists of three sub-networks:i) for removing rain streaks without remnants, we present a squeeze-and-excitation based rain residual network; ii) for encouraging the lost details to return, we construct a structure detail context aggregation based detail repair network; to our knowledge, this is the first time; and iii) for building efficient contrastive constraints for both rain streaks and clean backgrounds, we exploit a novel dual sample-augmented contrastive regularization network.Semi-DRDNet operates smoothly on both synthetic and real-world rainy data in terms of deraining robustness and detail accuracy. Comparisons on four datasets including our established Real200 show clear improvements of Semi-DRDNet over fifteen state-of-the-art methods. Code and dataset are available at https://github.com/syy-whu/DRD-Net.

翻译：雨天图像内容的复杂性常导致先进去雨网络出现图像退化，包括残留雨痕、错误去除细节以及失真外观。当将在合成数据上训练的模型应用于真实雨天图像时，这种退化会进一步加剧。我们观察到合成与真实雨天图像之间存在两类域差距：其一是雨纹模式差异，其二是无雨图像在像素级外观上的差异。为弥合这两个域差距，我们提出一种基于双样本增强对比学习的半监督细节恢复图像去雨网络（Semi-DRDNet）。Semi-DRDNet包含三个子网络：i) 针对无残留雨痕去除任务，提出基于压缩激励的雨痕残差网络；ii) 为促进丢失细节恢复，构建基于结构细节上下文聚合的细节修复网络（据我们所知，这是首次提出）；iii) 为同时建立针对雨痕和干净背景的高效对比约束，设计新型双样本增强对比正则化网络。在去雨鲁棒性和细节准确性方面，Semi-DRDNet能平滑处理合成与真实雨天数据。在包含我们建立的Real200数据集在内的四个数据集上的对比实验表明，Semi-DRDNet相较于十五种前沿方法具有显著优势。代码和数据集可在https://github.com/syy-whu/DRD-Net获取。