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

• URL: http://arxiv.org/abs/2106.02299v1
• Date: Fri, 4 Jun 2021 07:15:32 GMT
• Title: MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution
• Authors: Liying Lu, Wenbo Li, Xin Tao, Jiangbo Lu, Jiaya Jia
• Abstract summary: 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.
• Score: 74.24676600271253
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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