HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models
This work addresses the limitation of hyperspectral images for computer vision tasks by improving spatial resolution, though it appears incremental as it builds on existing diffusion model advancements.
The paper tackles the problem of low spatial resolution in hyperspectral images by proposing HSR-Diff, a super-resolution approach using conditional diffusion models that merges high-resolution multispectral images with low-resolution hyperspectral data, achieving state-of-the-art performance on four public datasets.
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.