Compared to daytime image deraining, nighttime image deraining poses significant challenges due to inherent complexities of nighttime scenarios and the lack of high-quality datasets that accurately represent the coupling effect between rain and illumination. In this paper, we rethink the task of nighttime image deraining and contribute a new high-quality benchmark, HQ-NightRain, which offers higher harmony and realism compared to existing datasets. In addition, we develop an effective Color Space Transformation Network (CST-Net) for better removing complex rain from nighttime scenes. Specifically, we propose a learnable color space converter (CSC) to better facilitate rain removal in the Y channel, as nighttime rain is more pronounced in the Y channel compared to the RGB color space. To capture illumination information for guiding nighttime deraining, implicit illumination guidance is introduced enabling the learned features to improve the model's robustness in complex scenarios. Extensive experiments show the value of our dataset and the effectiveness of our method. The source code and datasets are available at https://github.com/guanqiyuan/CST-Net.

翻译：与日间图像去雨相比，夜间图像去雨因夜间场景固有的复杂性以及缺乏能准确表征雨水与光照耦合效应的高质量数据集而面临显著挑战。本文重新审视了夜间图像去雨任务，并贡献了一个新的高质量基准数据集HQ-NightRain，该数据集相较于现有数据集具有更高的协调性与真实感。此外，我们开发了一种高效的色彩空间变换网络（CST-Net），以更好地去除夜间场景中的复杂雨纹。具体而言，我们提出了一种可学习的色彩空间转换器（CSC），以更好地促进Y通道中的雨纹去除，因为相较于RGB色彩空间，夜间雨纹在Y通道中更为明显。为捕捉用于指导夜间去雨的照明信息，我们引入了隐式照明引导机制，使学习到的特征能够提升模型在复杂场景中的鲁棒性。大量实验证明了我们数据集的价值以及我们方法的有效性。源代码与数据集可在 https://github.com/guanqiyuan/CST-Net 获取。