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:超光谱图像脱色对于应当适当考虑的高度相似和相互关联的光谱信息来说是独特的。然而,现有方法在探索不同波段和每个波段内部特征相互作用的频谱相关性方面显示出局限性。此外,低和高层次特征对于不同的空间光谱区域通常具有不同的重要性,而对于目前的算法也没有充分探讨这一点。在本文中,我们提出了一个混合注意网络,同时考虑不同波段之间和不同频段内部的相互关系以及低和高水平空间光谱有意义的特征之间的相互作用。具体地说,我们引入了多头频谱经常性关注,将所有光谱带的频谱特征有效融合在一起。这些特征通过探索光谱段内部的关系而逐步加强光谱频道的注意。此外,我们建议进行仔细的跳过式连接,以适应方式控制中低和高层次空间光谱特征的比例,以更好地增强综合特征。广泛的实验表明,我们的光谱系超越了所有光谱段段段之间的现有状态光谱系方法,同时维持低成本参数和运行时间。