Video restoration task aims to recover high-quality videos from low-quality
observations. This contains various important sub-tasks, such as video
denoising, deblurring and low-light enhancement, since video often faces
different types of degradation, such as blur, low light, and noise. Even worse,
these kinds of degradation could happen simultaneously when taking videos in
extreme environments. This poses significant challenges if one wants to remove
these artifacts at the same time. In this paper, to the best of our knowledge,
we are the first to propose an efficient end-to-end video transformer approach
for the joint task of video deblurring, low-light enhancement, and denoising.
This work builds a novel multi-tier transformer where each tier uses a
different level of degraded video as a target to learn the features of video
effectively. Moreover, we carefully design a new tier-to-tier feature fusion
scheme to learn video features incrementally and accelerate the training
process with a suitable adaptive weighting scheme. We also provide a new
Multiscene-Lowlight-Blur-Noise (MLBN) dataset, which is generated according to
the characteristics of the joint task based on the RealBlur dataset and YouTube
videos to simulate realistic scenes as far as possible. We have conducted
extensive experiments, compared with many previous state-of-the-art methods, to
show the effectiveness of our approach clearly.

我们是第一次提出了一种高效的端到端视频转换器方法，用于视频去模糊、低光增强和去噪的综合任务。我们构建了一个新的多层转换器，每一层使用不同级别的模糊视频作为目标，以有效地学习视频的特征；此外，我们还设计了一种新的层间特征融合方案，以逐步学习视频特征并加速训练过程。我们还提供了一个新的多场景低光模糊噪声（MLBN）数据集，根据联合任务的特点生成，以尽可能模拟真实场景。我们进行了大量的实验证明，与许多先前的最先进方法相比，我们的方法的有效性得到了清楚的展示。