  Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising
paradigm to enhance a low-resolution (LR) input image 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 produces explicit robust matching crossing
transformation and resolution. 1) For the transformation gap, we propose a
contrastive correspondence network, which learns transformation-robust
correspondences using augmented views of the input image. 2) For the resolution
gap, we adopt a 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. In addition, to faithfully evaluate the
performance of Ref-SR under a realistic setting, we contribute the
Webly-Referenced SR (WR-SR) dataset, mimicking the practical usage scenario.
Extensive experiments demonstrate that our proposed C2-Matching significantly
outperforms state of the arts by over 1dB on the standard CUFED5 benchmark.
Notably, it also shows great generalizability on WR-SR dataset as well as
robustness across large scale and rotation transformations.
