  Existing hyperspectral image (HSI) super-resolution (SR) methods struggle to
effectively capture the complex spectral-spatial relationships and low-level
details, while diffusion models represent a promising generative model known
for their exceptional performance in modeling complex relations and learning
high and low-level visual features. The direct application of diffusion models
to HSI SR is hampered by challenges such as difficulties in model convergence
and protracted inference time. In this work, we introduce a novel
Group-Autoencoder (GAE) framework that synergistically combines with the
diffusion model to construct a highly effective HSI SR model (DMGASR). Our
proposed GAE framework encodes high-dimensional HSI data into low-dimensional
latent space where the diffusion model works, thereby alleviating the
difficulty of training the diffusion model while maintaining band correlation
and considerably reducing inference time. Experimental results on both natural
and remote sensing hyperspectral datasets demonstrate that the proposed method
is superior to other state-of-the-art methods both visually and metrically.
