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.

翻译：现有高光谱图像超分辨率方法难以有效捕捉复杂的光谱-空间关系及低级细节，而扩散模型作为一种生成模型，在建模复杂关系和学习高低层视觉特征方面展现出卓越性能。将扩散模型直接应用于高光谱图像超分辨率面临模型收敛困难、推理时间过长等挑战。本文提出一种新型组自编码器框架，与扩散模型协同构建高效的高光谱图像超分辨率模型（DMGASR）。该框架将高维高光谱数据编码至低维潜空间，使扩散模型在此空间中运行，从而在降低训练难度的同时保持波段相关性并大幅缩短推理时间。在自然与遥感高光谱数据集上的实验结果表明，该方法在视觉质量和定量指标上均优于现有最先进方法。