  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.
