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

Chanyue Wu, Dong Wang, Yunpeng Bai, Hanyu Mao, Ying Li, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

3 Citations (SciVal)

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 (CDFormer) 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.
Original languageEnglish
Title of host publication2023 IEEE/CVF International Conference on Computer Vision (ICCV)
PublisherIEEE Press
Pages7060-7070
Number of pages11
ISBN (Print)979-8-3503-0718-4
DOIs
Publication statusPublished - 15 Jan 2024

Publication series

NameIEEE International Conference on Computer Vision
PublisherIEEE

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