Existing deep-learning-based methods for nighttime video deraining rely on synthetic data due to the absence of real-world paired data. However, the intricacies of the real world, particularly with the presence of light effects and low-light regions affected by noise, create significant domain gaps, hampering synthetic-trained models in removing rain streaks properly and leading to over-saturation and color shifts. Motivated by this, we introduce NightRain, a novel nighttime video deraining method with adaptive-rain-removal and adaptive-correction. Our adaptive-rain-removal uses unlabeled rain videos to enable our model to derain real-world rain videos, particularly in regions affected by complex light effects. The idea is to allow our model to obtain rain-free regions based on the confidence scores. Once rain-free regions and the corresponding regions from our input are obtained, we can have region-based paired real data. These paired data are used to train our model using a teacher-student framework, allowing the model to iteratively learn from less challenging regions to more challenging regions. Our adaptive-correction aims to rectify errors in our model's predictions, such as over-saturation and color shifts. The idea is to learn from clear night input training videos based on the differences or distance between those input videos and their corresponding predictions. Our model learns from these differences, compelling our model to correct the errors. From extensive experiments, our method demonstrates state-of-the-art performance. It achieves a PSNR of 26.73dB, surpassing existing nighttime video deraining methods by a substantial margin of 13.7%.

翻译：现有基于深度学习的夜间视频去雨方法因缺乏真实世界配对数据而依赖合成数据。然而，真实世界的复杂性——尤其是光照效应和受噪声影响的低光照区域——造成了显著的域间差异，导致合成训练模型无法有效去除雨纹，并引发过饱和与色彩偏移。受此启发，我们提出NightRain——一种采用自适应去雨与自适应校正的新型夜间视频去雨方法。自适应去雨模块利用无标签雨视频，使模型能够对真实雨视频进行去雨处理，尤其针对受复杂光照效应影响的区域。其核心思想是基于置信度得分使模型获取无雨区域，进而从输入中获得对应区域的无雨-有雨配对数据。这些配对数据通过教师-学生框架训练模型，使其能够从较易区域逐步学习至更具挑战性的区域。自适应校正模块旨在修正模型预测中的过饱和、色彩偏移等误差，其理念是基于清晰夜间输入训练视频与对应预测之间的差异（或距离）进行学习。模型通过捕捉这些差异来强制修正预测误差。大量实验表明，本方法实现了最先进的性能——PSNR达到26.73dB，较现有夜间视频去雨方法显著提升13.7%。