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Despite the proven significance of hyperspectral images (HSIs) in performingvarious computer vision tasks, its potential is adversely affected by thelow-resolution (LR) property in the spatial domain, resulting from multiplephysical factors. Inspired by recent advancements in deep generative models, wepropose an HSI Super-resolution (SR) approach with Conditional Diffusion Models(HSR-Diff) that merges a high-resolution (HR) multispectral image (MSI) withthe corresponding LR-HSI. HSR-Diff generates an HR-HSI via repeated refinement,in which the HR-HSI is initialized with pure Gaussian noise and iterativelyrefined. At each iteration, the noise is removed with a Conditional DenoisingTransformer (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 informationof full-resolution images. Systematic experiments have been conducted on fourpublic datasets, demonstrating that HSR-Diff outperforms state-of-the-artmethods.

HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models