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Since the number of incident energies is limited, it is difficult to directlyacquire hyperspectral images (HSI) with high spatial resolution. Consideringthe high dimensionality and correlation of HSI, super-resolution (SR) of HSIremains a challenge in the absence of auxiliary high-resolution images.Furthermore, it is very important to extract the spatial features effectivelyand make full use of the spectral information. This paper proposes a novel HSIsuper-resolution algorithm, termed dual-domain network based on hybridconvolution (SRDNet). Specifically, a dual-domain network is designed to fullyexploit the spatial-spectral and frequency information among the hyper-spectraldata. To capture inter-spectral self-similarity, a self-attention learningmechanism (HSL) is devised in the spatial domain. Meanwhile the pyramidstructure is applied to increase the acceptance field of attention, whichfurther reinforces the feature representation ability of the network. Moreover,to further improve the perceptual quality of HSI, a frequency loss(HFL) isintroduced to optimize the model in the frequency domain. The dynamic weightingmechanism drives the network to gradually refine the generated frequency andexcessive smoothing caused by spatial loss. Finally, In order to better fullyobtain the mapping relationship between high-resolution space andlow-resolution space, a hybrid module of 2D and 3D units with progressiveupsampling strategy is utilized in our method. Experiments on a widely usedbenchmark dataset illustrate that the proposed SRDNet method enhances thetexture information of HSI and is superior to state-of-the-art methods.

Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution