Despite the quality improvement brought by the recent methods, video
super-resolution (SR) is still very challenging, especially for videos that are
low-light and noisy. The current best solution is to subsequently employ best
models of video SR, denoising, and illumination enhancement, but doing so often
lowers the image quality, due to the inconsistency between the models. This
paper presents a new parametric representation called the Deep Parametric 3D
Filters (DP3DF), which incorporates local spatiotemporal information to enable
simultaneous denoising, illumination enhancement, and SR efficiently in a
single encoder-and-decoder network. Also, a dynamic residual frame is jointly
learned with the DP3DF via a shared backbone to further boost the SR quality.
We performed extensive experiments, including a large-scale user study, to show
our method's effectiveness. Our method consistently surpasses the best
state-of-the-art methods on all the challenging real datasets with top PSNR and
user ratings, yet having a very fast run time.

本文提出了一种新的参数化表示 Deep Parametric 3D Filters (DP3DF)，它融合了局部时空信息，并通过共享主干学习动态剩余帧，使得在单个编码器 - 解码器网络中同时实现降噪、光照增强和视频超分辨率，该方法在各种具有挑战性的真实数据集上始终优于最佳的现有方法，具有极快的运行时间。