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

MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution