Enhancing Hyperspectral Image Synthesis via the Spectral Super-resolution Post-Processing Network

Hyperspectral images (HSIs) provide rich spectral information essential for numerous remote sensing applications. However, the high cost and complexity of hyperspectral cameras make them difficult to deploy widely. A practical alternative is synthesizing HSI from readily available RGB images, addressing the limited accessibility of HSI data. In this paper, we propose a simple yet effective post-processing network for spectral super-resolution, which enhances the quality of initially generated hyperspectral images. Our method builds on existing CNN-based models, such as DenseUnet, CanNet, and SSDCN, to produce preliminary HSIs by capturing local spatial features. To improve spectral accuracy and overall image quality, we introduce a post-processing stage using the HyperSIGMA foundation model, pre-trained on a large remote sensing dataset, to refine the preliminary HSIs by leveraging global spatial and spectral relationships. Experiments on the DFC2018 dataset show that our post-processing network significantly improves both spatial and spectral fidelity. Quantitative evaluations using PSNR, ERGAS, and SAM metrics confirm the superiority of our two-stage framework, particularly in enhancing spectral reconstruction. These results highlight the potential of foundation models as post-processing networks for spectral super-resolution, providing an accessible and effective solution to HSI data scarcity in remote sensing applications.