  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%.
