SDG-DSB: Spectral Degradation Guided Diffusion Schrödinger Bridge for Hyperspectral Images Super-Resolution

Hyperspectral image (HSI) super-resolution plays a crucial role in enhancing the spatial resolution of HSIs while preserving spectral integrity. A common approach for HSI super-resolution involves fusing low-resolution HSIs (LR-HSIs) with high-resolution multispectral images (HR-MSIs). In this paper, we propose a novel spectral degradation guided Diffusion Schrödinger Bridge (SDG-DSB) model specifically designed for HSI super-resolution. Recognizing that LR-HSIs contain complete spectral information, we leverage a subspace model to decompose LR-HSIs into endmembers and LR abundance maps. To reconstruct the HR-HSI, we propose a Diffusion Schrödinger Bridge, transforming LR abundance maps into high-resolution counterparts. This process is supported by multiple pre-trained Diffusion Schrödinger Bridge generators trained on natural images, integrating spatial priors for improved accuracy. Additionally, the HR-MSI guides the diffusion process, maintaining consistency with the spectral degradation path from HR-HSI to HR-MSI. This spectral degradation captures the inherent correlations between spectral bands, allowing for a more accurate restoration of high-frequency spatial details while maintaining the integrity of spectral information. Finally, the HR abundance maps are combined with endmembers extracted from LR-HSI to generate the HR-HSI. Extensive experiments on benchmark HSI datasets show that the proposed SDG-DSB outperforms state-of-the-art methods, demonstrating its effectiveness in both spatial and spectral reconstruction.