  Reference-based image super-resolution (RefSR) has shown promising success in
recovering high-frequency details by utilizing an external reference image
(Ref). In this task, texture details are transferred from the Ref image to the
low-resolution (LR) image according to their point- or patch-wise
correspondence. Therefore, high-quality correspondence matching is critical. It
is also desired to be computationally efficient. Besides, existing RefSR
methods tend to ignore the potential large disparity in distributions between
the LR and Ref images, which hurts the effectiveness of the information
utilization. In this paper, we propose the MASA network for RefSR, where two
novel modules are designed to address these problems. The proposed Match &
Extraction Module significantly reduces the computational cost by a
coarse-to-fine correspondence matching scheme. The Spatial Adaptation Module
learns the difference of distribution between the LR and Ref images, and remaps
the distribution of Ref features to that of LR features in a spatially adaptive
way. This scheme makes the network robust to handle different reference images.
Extensive quantitative and qualitative experiments validate the effectiveness
of our proposed model.
