Hyperspectral image denoising is unique for the highly similar and correlated spectral information that should be properly considered. However, existing methods show limitations in exploring the spectral correlations across different bands and feature interactions within each band. Besides, the low- and high-level features usually exhibit different importance for different spatial-spectral regions, which is not fully explored for current algorithms as well. In this paper, we present a Mixed Attention Network (MAN) that simultaneously considers the inter- and intra-spectral correlations as well as the interactions between low- and high-level spatial-spectral meaningful features. Specifically, we introduce a multi-head recurrent spectral attention that efficiently integrates the inter-spectral features across all the spectral bands. These features are further enhanced with a progressive spectral channel attention by exploring the intra-spectral relationships. Moreover, we propose an attentive skip-connection that adaptively controls the proportion of the low- and high-level spatial-spectral features from the encoder and decoder to better enhance the aggregated features. Extensive experiments show that our MAN outperforms existing state-of-the-art methods on simulated and real noise settings while maintaining a low cost of parameters and running time.

翻译：高光谱图像去噪的独特性在于其高度相似且相关的光谱信息需要被妥善处理。然而，现有方法在探索不同波段间的光谱相关性以及各波段内的特征交互方面存在局限性。此外，低层与高层特征对不同空间-光谱区域的重要性往往不同，这一特性在当前算法中也未得到充分挖掘。本文提出了一种混合注意力网络（MAN），该网络同时考虑了光谱间与光谱内的相关性，以及低层与高层空间-光谱有意义特征之间的交互。具体而言，我们引入了一种多头循环光谱注意力机制，能够高效整合所有光谱波段间的光谱间特征。这些特征进一步通过渐进式光谱通道注意力机制，通过探索光谱内关系得到增强。此外，我们提出了一种注意力跳跃连接，可自适应控制编码器与解码器中低层与高层空间-光谱特征的融合比例，从而更好地增强聚合特征。大量实验表明，我们的MAN在模拟和真实噪声场景下均优于现有最优方法，同时保持了较低的参数成本和运行时间。