Single hyperspectral image super-resolution (HIS) aims to improve the spatial resolution of a hyperspectral image without relying on auxiliary information. By taking advantage of the high similarity among neighbor bands, some recent methods have used a recursive structure to super-resolve a hyperspectral image band-by-band. They are usually memory-efficient and perform well. However, they tend to introduce feedback information without distinction so as to weaken the utilization of complementary information in the context. In addition, the spectral structure is inevitably destroyed when spatial information is extracted from neighbor bands, which hampers the effective exploration of spectral information in the subsequent process. To this end, we propose a two-stage network based on neighbor spectra maintenance and context affinity enhancement (AE), which is composed of two subnetworks: neighbor network and context network. The former uses several neighbor bands to generate the neighbor spatial–spectral feature, incorporating a parallel processing scheme designed to reduce spectral distortion. Then we construct a relationship representation between the neighbor feature and feedback context information in the context network. By referring to the representation, the contents with higher complementarity will be highlighted in this stage. Experimental results on five public hyperspectral image datasets demonstrate that the proposed network not only outperforms state-of-the-art methods in terms of spatial reconstruction accuracy and spectral fidelity but also requires less memory usage.

中文翻译：

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单高光谱图像超分辨率的邻近光谱维护和上下文亲和力增强

单张高光谱图像超分辨率（HIS）旨在不依赖辅助信息提高高光谱图像的空间分辨率。通过利用相邻波段之间的高度相似性，最近的一些方法使用递归结构来逐波段地超分辨高光谱图像。它们通常内存效率高且性能良好。然而，它们往往不加区分地引入反馈信息，从而削弱了上下文中补充信息的利用。此外，从邻近波段提取空间信息时不可避免地会破坏光谱结构，这阻碍了后续过程中光谱信息的有效探索。为此，我们提出了一种基于邻居谱维护和上下文亲和力增强（AE）的两级网络，该网络由两个子网络组成：邻居网络和上下文网络。前者使用多个相邻频带来生成相邻空间光谱特征，并结合旨在减少光谱失真的并行处理方案。然后我们在上下文网络中构建邻居特征和反馈上下文信息之间的关系表示。通过参考代表性，本阶段将突出互补性较高的内容。五个公共高光谱图像数据集的实验结果表明，所提出的网络不仅在空间重建精度和光谱保真度方面优于最先进的方法，而且需要更少的内存使用。