Abstract
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.
Abstract (translated)
基于参考的超分辨率（RefSR）方法具有在遥感图像的空间和时间分辨率之间建立桥梁的潜力。然而，现有的RefSR方法在内容重建的忠实度和在大规模因子中的纹理传输的有效性方面存在局限。条件扩散模型为生成高分辨率图像开辟了新的机会，但在这些模型中有效地利用参考图像仍然是一个需要进一步研究的问题。此外，在没有相关参考信息的地方，内容保真度很难保证。为解决这些问题，我们提出了一个名为Ref-Diff的基于变化的RefSR模型，使用土地覆盖变化 prior 指导去噪过程。具体来说，我们将 prior 注入到去噪模型中，以提高在未改变区域中参考信息的利用效率并调节在改变区域中语义相关内容的重建。借助这一强大的指导，我们解耦了语义引导去噪和参考纹理引导去噪过程，从而提高模型性能。大量实验证明，与最先进的RefSR方法相比，所提出的方法在数量和质量评估方面具有卓越的效力和鲁棒性。代码和数据可在此链接下载：https://www.xxxxxx.com/
URL
https://arxiv.org/abs/2403.17460
