RGB Images Enhancing Hyperspectral Image Denoising with Diffusion Model

Deep learning (DL)-based hyperspectral image (HSI) denoising has achieved remarkable accomplishments but is still limited by insufficient training data of noisy-clean pairs. This paper proposes a novel approach to enhance the diffusion model (DM)-based HSI denoising by leveraging abundant RGB images. Specifically, an RGB-DM is pre-trained on the RGB images to capture comprehensive spatial information, and then it is integrated with the HSI-DM using a fusion operator during the reverse diffusion process, yielding improved denoising results. To optimize this fusion process, we derive the optimal fusion weight by minimizing signal distortion. Experimental results on CAVE and ICVL datasets demonstrate the effectiveness of our approach by outperforming state-of-the-art methods.