Spatial-Spectral Transformer for Hyperspectral Image Denoising

• URL: http://arxiv.org/abs/2211.14090v1
• Date: Fri, 25 Nov 2022 13:18:45 GMT
• Title: Spatial-Spectral Transformer for Hyperspectral Image Denoising
• Authors: Miaoyu Li, Ying Fu, Yulun Zhang
• Abstract summary: Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications. Existing convolution-based methods face the trade-off between computational efficiency and capability to model non-local characteristics of HSI. We propose a Spatial-Spectral Transformer (SST) to alleviate this problem.
• Score: 31.86587556847128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, 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.

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