HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion
  Models
Chanyue Wu Dong Wang Hanyu Mao Ying Li
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.