  Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising
paradigm to enhance a low-resolution (LR) input image or video by introducing
an additional high-resolution (HR) reference image. Existing Ref-SR methods
mostly rely on implicit correspondence matching to borrow HR textures from
reference images to compensate for the information loss in input images.
However, performing local transfer is difficult because of two gaps between
input and reference images: the transformation gap (e.g., scale and rotation)
and the resolution gap (e.g., HR and LR). To tackle these challenges, we
propose C2-Matching in this work, which performs explicit robust matching
crossing transformation and resolution. 1) To bridge the transformation gap, we
propose a contrastive correspondence network, which learns
transformation-robust correspondences using augmented views of the input image.
2) To address the resolution gap, we adopt teacher-student correlation
distillation, which distills knowledge from the easier HR-HR matching to guide
the more ambiguous LR-HR matching. 3) Finally, we design a dynamic aggregation
module to address the potential misalignment issue between input images and
reference images. In addition, to faithfully evaluate the performance of
Reference-based Image Super-Resolution under a realistic setting, we contribute
the Webly-Referenced SR (WR-SR) dataset, mimicking the practical usage
scenario. We also extend C2-Matching to Reference-based Video Super-Resolution
task, where an image taken in a similar scene serves as the HR reference image.
Extensive experiments demonstrate that our proposed C2-Matching significantly
outperforms state of the arts on the standard CUFED5 benchmark and also boosts
the performance of video SR by incorporating the C2-Matching component into
Video SR pipelines.
