Since the number of incident energies is limited, it is difficult to directly acquire hyperspectral images (HSI) with high spatial resolution. Considering the high dimensionality and correlation of HSI, super-resolution (SR) of HSI remains a challenge in the absence of auxiliary high-resolution images. Furthermore, it is very important to extract the spatial features effectively and make full use of the spectral information. This paper proposes a novel HSI super-resolution algorithm, termed dual-domain network based on hybrid convolution (SRDNet). Specifically, a dual-domain network is designed to fully exploit the spatial-spectral and frequency information among the hyper-spectral data. To capture inter-spectral self-similarity, a self-attention learning mechanism (HSL) is devised in the spatial domain. Meanwhile the pyramid structure is applied to increase the acceptance field of attention, which further reinforces the feature representation ability of the network. Moreover, to further improve the perceptual quality of HSI, a frequency loss(HFL) is introduced to optimize the model in the frequency domain. The dynamic weighting mechanism drives the network to gradually refine the generated frequency and excessive smoothing caused by spatial loss. Finally, In order to better fully obtain the mapping relationship between high-resolution space and low-resolution space, a hybrid module of 2D and 3D units with progressive upsampling strategy is utilized in our method. Experiments on a widely used benchmark dataset illustrate that the proposed SRDNet method enhances the texture information of HSI and is superior to state-of-the-art methods.

翻译：由于入射能量有限，直接获取高空间分辨率的超光谱图像（HSI）较为困难。考虑到HSI的高维性和相关性，在缺乏辅助高分辨率图像的情况下，HSI的超分辨率（SR）仍是一项挑战。此外，有效提取空间特征并充分利用光谱信息至关重要。本文提出了一种新型HSI超分辨率算法，即基于混合卷积的双域网络（SRDNet）。具体而言，设计了一个双域网络，以充分挖掘超光谱数据中的空间-光谱与频率信息。为捕获光谱间的自相似性，在空间域中设计了一种自注意力学习机制（HSL）。同时，采用金字塔结构增大注意力的接受域，进一步增强网络的特征表示能力。此外，为提升HSI的感知质量，引入频率损失（HFL）在频域中优化模型，动态加权机制驱动网络逐步优化生成的频率分量，并缓解空间损失导致的过度平滑问题。最后，为更好获取高分辨率空间与低分辨率空间之间的映射关系，本方法采用了渐进上采样策略的2D与3D单元混合模块。在广泛使用的基准数据集上的实验表明，所提SRDNet方法增强了HSI的纹理信息，并优于现有最先进方法。