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

• URL: http://arxiv.org/abs/2306.12085v1
• Date: Wed, 21 Jun 2023 08:04:30 GMT
• Title: HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models
• Authors: Chanyue Wu, Dong Wang, Hanyu Mao, Ying Li
• Abstract summary: We propose an HSI Super-resolution (SR) approach with Conditional Diffusion Models (HSR-Diff) HSR-Diff generates an HR-HSI via repeated refinement, in which the HR-HSI is spatial with pure Gaussian noise and iteratively refined. In addition, a progressive learning strategy is employed to exploit the global information of full-resolution images.
• Score: 10.865272587124027
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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