  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.
