Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing the great power of deep learning, existing HSI denoising methods suffer from limitations in capturing the non-local self-similarity. Transformers have shown potential in capturing long-range dependencies, but few attempts have been made with specifically designed Transformer to model the spatial and spectral correlation in HSIs. In this paper, we address these issues by proposing a spectral enhanced rectangle Transformer, driving it to explore the non-local spatial similarity and global spectral low-rank property of HSIs. For the former, we exploit the rectangle self-attention horizontally and vertically to capture the non-local similarity in the spatial domain. For the latter, we design a spectral enhancement module that is capable of extracting global underlying low-rank property of spatial-spectral cubes to suppress noise, while enabling the interactions among non-overlapping spatial rectangles. Extensive experiments have been conducted on both synthetic noisy HSIs and real noisy HSIs, showing the effectiveness of our proposed method in terms of both objective metric and subjective visual quality. The code is available at https://github.com/MyuLi/SERT.

翻译：去噪是高光谱图像应用中的关键步骤。尽管深度学习展现了强大能力，但现有高光谱图像去噪方法在捕捉非局部自相似性方面存在局限性。Transformer在捕获长距离依赖关系方面展现出潜力，但少有研究专门设计Transformer来建模高光谱图像中的空间和光谱相关性。本文针对这些问题，提出一种光谱增强矩形Transformer，驱动其探索高光谱图像的非局部空间相似性和全局光谱低秩属性。对于前者，我们利用水平与垂直方向的矩形自注意力机制捕获空间域的非局部相似性；对于后者，我们设计了一个光谱增强模块，该模块能够提取空间-光谱立方体的全局潜在低秩属性以抑制噪声，同时实现非重叠空间矩形之间的交互。我们在合成噪声高光谱图像和真实噪声高光谱图像上进行了大量实验，结果表明所提方法在客观指标和主观视觉质量方面均具有有效性。代码已开源至https://github.com/MyuLi/SERT。