Spatial-Spectral Transformer for Hyperspectral Image Denoising

Miaoyu Li, Ying Fu*, Yulun Zhang

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

37 引用 (Scopus)

摘要

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications. Unfortunately, though witnessing the development of deep learning in HSI denoising area, existing convolution-based methods face the trade-off between computational efficiency and capability to model non-local characteristics of HSI. In this paper, we propose a Spatial-Spectral Transformer (SST) to alleviate this problem. To fully explore intrinsic similarity characteristics in both spatial dimension and spectral dimension, we conduct non-local spatial self-attention and global spectral self-attention with Transformer architecture. The window-based spatial self-attention focuses on the spatial similarity beyond the neighboring region. While, the spectral self-attention exploits the long-range dependencies between highly correlative bands. Experimental results show that our proposed method outperforms the state-of-the-art HSI denoising methods in quantitative quality and visual results. The code is released at https://github.com/MyuLi/SST.

源语言英语
主期刊名AAAI-23 Technical Tracks 1
编辑Brian Williams, Yiling Chen, Jennifer Neville
出版商AAAI press
1368-1376
页数9
ISBN(电子版)9781577358800
出版状态已出版 - 27 6月 2023
活动37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, 美国
期限: 7 2月 202314 2月 2023

出版系列

姓名Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
37

会议

会议37th AAAI Conference on Artificial Intelligence, AAAI 2023
国家/地区美国
Washington
时期7/02/2314/02/23

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引用此

Li, M., Fu, Y., & Zhang, Y. (2023). Spatial-Spectral Transformer for Hyperspectral Image Denoising. 在 B. Williams, Y. Chen, & J. Neville (编辑), AAAI-23 Technical Tracks 1 (页码 1368-1376). (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; 卷 37). AAAI press.