  We present a novel approach to reference-based super-resolution (RefSR) with
the focus on dual-camera super-resolution (DCSR), which utilizes reference
images for high-quality and high-fidelity results. Our proposed method
generalizes the standard patch-based feature matching with spatial alignment
operations. We further explore the dual-camera super-resolution that is one
promising application of RefSR, and build a dataset that consists of 146 image
pairs from the main and telephoto cameras in a smartphone. To bridge the domain
gaps between real-world images and the training images, we propose a
self-supervised domain adaptation strategy for real-world images. Extensive
experiments on our dataset and a public benchmark demonstrate clear improvement
achieved by our method over state of the art in both quantitative evaluation
and visual comparisons.
