Recently, reference-based image super-resolution (RefSR) has shown excellent performance in image super-resolution (SR) tasks. The main idea of RefSR is to utilize additional information from the reference (Ref) image to recover the high-frequency components in low-resolution (LR) images. By transferring relevant textures through feature matching, RefSR models outperform existing single image super-resolution (SISR) models. However, their performance significantly declines when a domain gap between Ref and LR images exists, which often occurs in real-world scenarios, such as satellite imaging. In this letter, we introduce a Domain Matching (DM) module that can be seamlessly integrated with existing RefSR models to enhance their performance in a plug-and-play manner. To the best of our knowledge, we are the first to explore Domain Matching-based RefSR in remote sensing image processing. Our analysis reveals that their domain gaps often occur in different satellites, and our model effectively addresses these challenges, whereas existing models struggle. Our experiments demonstrate that the proposed DM module improves SR performance both qualitatively and quantitatively for remote sensing super-resolution tasks.

翻译：近期，基于参考图像的超分辨率（RefSR）方法在图像超分辨率（SR）任务中展现出优异性能。RefSR的核心思想是利用参考（Ref）图像中的额外信息来恢复低分辨率（LR）图像的高频成分。通过特征匹配迁移相关纹理，RefSR模型性能优于现有单幅图像超分辨率（SISR）模型。然而，当Ref图像与LR图像之间存在域差距时——这在真实场景（如卫星成像）中尤为常见——其性能会显著下降。本文提出一种域匹配（DM）模块，能以即插即用方式无缝集成至现有RefSR模型以增强其性能。据我们所知，这是首个探索基于域匹配的RefSR在遥感图像处理中应用的研究。分析表明，域差距常出现在不同卫星数据之间，本文提出的模型有效解决了现有模型难以应对的这些挑战。实验证明，所提出的DM模块在遥感超分辨率任务中，无论是定性还是定量指标均提升了SR性能。