Recently, single gray/RGB image super-resolution reconstruction task has been extensively studied and made significant progress by leveraging the advanced machine learning techniques based on deep convolutional neural networks (DCNNs). However, there has been limited technical development focusing on single hyperspectral image super-resolution due to the high-dimensional and complex spectral patterns in hyperspectral image. In this article, we make a step forward by investigating how to adapt state-of-the-art deep learning based single gray/RGB image super-resolution approaches for computationally efficient single hyperspectral image super-resolution, referred as SSPSR. Specifically, we introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Considering that the hyperspectral training samples are scarce and the spectral dimension of hyperspectral image data is very high, it is nontrivial to train a stable and effective deep network. Therefore, a group convolution (with shared network parameters) and progressive upsampling framework is proposed. This will not only alleviate the difficulty in feature extraction due to high dimension of the hyperspectral data, but also make the training process more stable. To exploit the spatial and spectral prior, we design a spatial-spectral block (SSB), which consists of a spatial residual module and a spectral attention residual module. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images, and outperforms state-of-the-arts. The source code is available at [Online]. Available: https://github.com/junjun-jiang/SSPSR.

中文翻译：

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学习用于高光谱图像超分辨率的光谱空间先验

最近，通过利用基于深度卷积神经网络 (DCNN) 的先进机器学习技术，单灰度/RGB 图像超分辨率重建任务得到了广泛的研究并取得了重大进展。然而，由于高光谱图像中的高维和复杂光谱模式，专注于单张高光谱图像超分辨率的技术发展有限。在本文中，我们通过研究如何采用最先进的基于深度学习的单灰度/RGB 图像超分辨率方法来实现计算高效的单高光谱图像超分辨率（称为 SSPSR），从而向前迈进了一步。具体来说，我们引入了空间光谱先验网络（SSPN）来充分利用空间信息和高光谱数据光谱之间的相关性。考虑到高光谱训练样本稀少且高光谱图像数据的光谱维数非常高，训练一个稳定有效的深度网络并非易事。因此，提出了组卷积（具有共享网络参数）和渐进式上采样框架。这样不仅可以缓解高光谱数据维数高导致特征提取的难度，还可以使训练过程更加稳定。为了利用空间和光谱先验，我们设计了一个空间光谱块（SSB），它由空间残差模块和光谱注意力残差模块组成。一些高光谱图像的实验结果表明，所提出的 SSPSR 方法增强了恢复的高分辨率高光谱图像的细节，并且优于现有技术。源代码可在 [在线] 处获得。可用：https://github.com/junjun-jiang/SSPSR。