论文标题
高光谱图像Denoising的空间光谱变压器
Spatial-Spectral Transformer for Hyperspectral Image Denoising
论文作者
论文摘要
高光谱图像（HSI）denoising是随后的HSI应用的关键预处理程序。不幸的是，尽管目睹了HSI DeNoising地区深度学习的发展，但现有的基于卷积的方法面临着计算效率和对HSI非本地特征进行建模能力之间的权衡。在本文中，我们提出了一个空间光谱变压器（SST）来减轻此问题。为了充分探索空间维度和频谱维度的内在相似性特征，我们通过变压器结构进行非本地空间自我注意和全球光谱自我注意。基于窗口的空间自我发作的重点是邻近区域以外的空间相似性。同时，光谱自我发项利用高度相关频段之间的远程依赖性。实验结果表明，我们所提出的方法优于定量质量和视觉结果的最先进的HSI DeNoising方法。
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.
