SFSR: Spectral Fusion Super-Resolution for Multisensor Remote Sensing With Degraded References

Reference-based super-resolution (RefSR) aims to enhance low-resolution (LR) imagery by leveraging auxiliary reference observations. While effective under controlled settings, most existing RefSR methods implicitly assume that reference images are clean, well-aligned, and geometrically consistent with the target input. In real-world remote sensing systems, however, reference observations are frequently degraded by sensor noise, atmospheric blur, and geometric inconsistencies caused by different viewing angles and acquisition times. Moreover, due to the inherent resolution gap between LR and reference images, strict spectral consistency is difficult to guarantee in practice. These factors substantially reduce the reliability of reference cues and limit the applicability of RefSR in multi-sensor satellite imaging scenarios. To address these challenges, we propose Spectral Fusion Super-Resolution (SFSR), a diffusion-based RefSR framework designed to operate robustly under degraded reference conditions. At its core, SFSR introduces the Spectral Swin Cross-Attention Module (S2CAM), which enables frequency-aware reference utilization and integrates the refined reference features as conditional guidance within the reverse diffusion process. By explicitly redistributing spectral components and suppressing unreliable high-frequency responses introduced by noise, SFSR enables stable and effective use of reference information that conventional RefSR methods struggle to exploit. Extensive experiments on synthetic and benchmark satellite datasets demonstrate that SFSR consistently outperforms state-of-the-art RefSR approaches in terms of PSNR, SSIM, and perceptual metrics, while maintaining high visual fidelity under severe degradation. In addition, evaluations on downstream tasks such as object detection and semantic segmentation show that SFSR leads to clear performance improvements, confirming its robustness and practical value for real-world multi-sensor remote sensing applications.