  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.
