We propose the first reference-based video super-resolution (RefVSR) approach
that utilizes reference videos for high-fidelity results. We focus on RefVSR in
a triple-camera setting, where we aim at super-resolving a low-resolution
ultra-wide video utilizing wide-angle and telephoto videos. We introduce the
first RefVSR network that recurrently aligns and propagates temporal reference
features fused with features extracted from low-resolution frames. To
facilitate the fusion and propagation of temporal reference features, we
propose a propagative temporal fusion module. For learning and evaluation of
our network, we present the first RefVSR dataset consisting of triplets of
ultra-wide, wide-angle, and telephoto videos concurrently taken from triple
cameras of a smartphone. We also propose a two-stage training strategy fully
utilizing video triplets in the proposed dataset for real-world 4x video
super-resolution. We extensively evaluate our method, and the result shows the
state-of-the-art performance in 4x super-resolution.

该研究提出利用引用视频进行超分辨率处理的引用视频超分辨率（RefVSR）网络模型，实验结果表明，该模型在三个摄像头的超宽视频三重组成的数据集上表现出了最先进的 4x 超分辨率性能。