DDSR: Degradation-Aware Diffusion Model for Spectral Reconstruction from RGB Images

The reconstruction of hyperspectral images (HSIs) from RGB images is an attractive low-cost approach to recover hyperspectral information. However, existing approaches focus on learning an end-to-end mapping of RGB images and their corresponding HSIs with neural networks, which makes it difficult to ensure generalization due to the fact that they are trained on data with a specific degradation process. As a new paradigm of generative models, the diffusion model has shown great potential in image restoration, especially in noisy contexts. To address the unstable generalization ability of end-to-end models while exploiting the powerful ability of the diffusion model, we propose a degradation-aware diffusion model. The degradation process from HSI to RGB is modeled as a combination of multiple degradation operators, which are used to guide the inverse process of the diffusion model by utilizing a degradation-aware correction. By integrating the degradation-aware correction to the diffusion model, we obtain an efficient solver for spectral reconstruction, which is robust to different degradation patterns. Experiment results on various public datasets demonstrate that our method achieves competitive performance and shows a promising generalization ability.