  Despite the proven significance of hyperspectral images (HSIs) in performing
various computer vision tasks, its potential is adversely affected by the
low-resolution (LR) property in the spatial domain, resulting from multiple
physical factors. Inspired by recent advancements in deep generative models, we
propose an HSI Super-resolution (SR) approach with Conditional Diffusion Models
(HSR-Diff) that merges a high-resolution (HR) multispectral image (MSI) with
the corresponding LR-HSI. HSR-Diff generates an HR-HSI via repeated refinement,
in which the HR-HSI is initialized with pure Gaussian noise and iteratively
refined. At each iteration, the noise is removed with a Conditional Denoising
Transformer (CDF ormer) that is trained on denoising at different noise levels,
conditioned on the hierarchical feature maps of HR-MSI and LR-HSI. In addition,
a progressive learning strategy is employed to exploit the global information
of full-resolution images. Systematic experiments have been conducted on four
public datasets, demonstrating that HSR-Diff outperforms state-of-the-art
methods.
