Controllable Reference-Based Real-World Remote Sensing Image Super-Resolution with Generative Diffusion Priors

• URL: http://arxiv.org/abs/2506.23801v1
• Date: Mon, 30 Jun 2025 12:45:28 GMT
• Title: Controllable Reference-Based Real-World Remote Sensing Image Super-Resolution with Generative Diffusion Priors
• Authors: Ce Wang, Wanjie Sun,
• Abstract summary: Super-resolution (SR) techniques can enhance the spatial resolution of remote sensing images by utilizing low-resolution (LR) images to reconstruct high-resolution (HR) images.<n>Existing RefSR methods struggle with real-world complexities, such as cross-sensor resolution gap and significant land cover changes.<n>We propose CRefDiff, a novel controllable reference-based diffusion model for real-world remote sensing image SR.
• Score: 13.148815217684277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Super-resolution (SR) techniques can enhance the spatial resolution of remote sensing images by utilizing low-resolution (LR) images to reconstruct high-resolution (HR) images, enabling more efficient large-scale earth observation applications. While single-image super-resolution (SISR) methods have shown progress, reference-based super-resolution (RefSR) offers superior performance by incorporating historical HR images alongside current LR observations. However, existing RefSR methods struggle with real-world complexities, such as cross-sensor resolution gap and significant land cover changes, often leading to under-generation or over-reliance on reference image. To address these challenges, we propose CRefDiff, a novel controllable reference-based diffusion model for real-world remote sensing image SR. To address the under-generation problem, CRefDiff is built upon the pretrained Stable Diffusion model, leveraging its powerful generative prior to produce accurate structures and textures. To mitigate over-reliance on the reference, we introduce a dual-branch fusion mechanism that adaptively integrates both local and global information from the reference image. Moreover, this novel dual-branch design enables reference strength control during inference, enhancing interactivity and flexibility of the model. Finally, a strategy named Better Start is proposed to significantly reduce the number of denoising steps, thereby accelerating the inference process. To support further research, we introduce Real-RefRSSRD, a new real-world RefSR dataset for remote sensing images, consisting of HR NAIP and LR Sentinel-2 image pairs with diverse land cover changes and significant temporal gaps. Extensive experiments on Real-RefRSSRD show that CRefDiff achieves state-of-the-art performance across various metrics and improves downstream tasks such as scene classification and semantic segmentation.

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