  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.
