  Reference-based super-resolution (RefSR) has the potential to build bridges
across spatial and temporal resolutions of remote sensing images. However,
existing RefSR methods are limited by the faithfulness of content
reconstruction and the effectiveness of texture transfer in large scaling
factors. Conditional diffusion models have opened up new opportunities for
generating realistic high-resolution images, but effectively utilizing
reference images within these models remains an area for further exploration.
Furthermore, content fidelity is difficult to guarantee in areas without
relevant reference information. To solve these issues, we propose a
change-aware diffusion model named Ref-Diff for RefSR, using the land cover
change priors to guide the denoising process explicitly. Specifically, we
inject the priors into the denoising model to improve the utilization of
reference information in unchanged areas and regulate the reconstruction of
semantically relevant content in changed areas. With this powerful guidance, we
decouple the semantics-guided denoising and reference texture-guided denoising
processes to improve the model performance. Extensive experiments demonstrate
the superior effectiveness and robustness of the proposed method compared with
state-of-the-art RefSR methods in both quantitative and qualitative
evaluations. The code and data are available at
https://github.com/dongrunmin/RefDiff.
