Group Shuffle and Spectral-Spatial Fusion for Hyperspectral Image Super-Resolution

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Recently, super-resolution (SR) tasks for single hyperspectral images have been extensively investigated and significant progress has been made by introducing advanced deep learning-based methods. However, hyperspectral image SR is still a challenging problem because of the numerous narrow and successive spectral bands of hyperspectral images. Existing methods adopt the group reconstruction mode to avoid the unbearable computational complexity brought by the high spectral dimensionality. Nevertheless, the group data lose the spectral responses in other ranges and preserve the information redundancy caused by continuous and similar spectrograms, thus containing too little information. In this paper, we propose a novel single hyperspectral image SR method named GSSR, which pioneers the exploration of tweaking spectral band sequence to improve the reconstruction effect. Specifically, we design the group shuffle that leverages interval sampling to produce new groups for separating adjacent and extremely similar bands. In this way, each group of data has more varied spectral responses and less redundant information. After the group shuffle, the spectral-spatial feature fusion block is employed to exploit the spectral-spatial features. To compensate for the adjustment of spectral order by the group shuffle, the local spectral continuity constraint module is subsequently appended to constrain the features for ensuring the spectral continuity. Experimental results on both natural and remote sensing hyperspectral images demonstrate that the proposed method achieves the best performance compared to the state-of-the-art methods.