PSAM: Progressive Spatial Adaptive Matching for Reference-Based Super Resolution

Reference-based super-resolution (RefSR), which aims to introduce an additional high-resolution (HR) reference (Ref) image to improve the reconstruction performance of low-resolution (LR) image, has achieved great success. Existing RefSR methods rely on the texture information of the reference image to compensate for the missing information. However, the differences of scale and orientation are unavoidable when obtaining useful information from the Ref image. In addition, it is difficult to achieve a good match due to the ill-posed between the LR image and Ref image. To address these challenges, we propose a new matching module, named progressive spatial adaptation module (PSAM). PSAM is a progressive alignment model to effectively overcome the ill-pose between the LR image and the Ref image. Further, we propose a spatial correction module (SCM) to correct for scale and orientation. Meanwhile, we introduce a gradient map to further correct the matched features. In addition, we propose a new loss function MC-Loss to ensure the success of correction. Experiments show that the matching method using PSAM to directly replace the existing RefSR is significantly better than the original matching method in terms of both quantitative and qualitative results.