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
this https URL

利用土地覆盖变化的先验信息来指导去噪过程的变化感知扩散模型用于参考图像超分辨率重建，通过将先验信息注入去噪模型，提高未改变区域参考信息的利用和已改变区域语义相关内容的重建，以此来改善模型性能。