The rapid development of deep learning provides a better solution for the
end-to-end reconstruction of hyperspectral image (HSI). However, existing
learning-based methods have two major defects. Firstly, networks with
self-attention usually sacrifice internal resolution to balance model
performance against complexity, losing fine-grained high-resolution (HR)
features. Secondly, even if the optimization focusing on spatial-spectral
domain learning (SDL) converges to the ideal solution, there is still a
significant visual difference between the reconstructed HSI and the truth.
Therefore, we propose a high-resolution dual-domain learning network (HDNet)
for HSI reconstruction. On the one hand, the proposed HR spatial-spectral
attention module with its efficient feature fusion provides continuous and fine
pixel-level features. On the other hand, frequency domain learning (FDL) is
introduced for HSI reconstruction to narrow the frequency domain discrepancy.
Dynamic FDL supervision forces the model to reconstruct fine-grained
frequencies and compensate for excessive smoothing and distortion caused by
pixel-level losses. The HR pixel-level attention and frequency-level refinement
in our HDNet mutually promote HSI perceptual quality. Extensive quantitative
and qualitative evaluation experiments show that our method achieves SOTA
performance on simulated and real HSI datasets. Code and models will be
released at this https URL

本文提出了一个高分辨率双域学习网络（HDNet）用于高光谱图像的重建，该网络采用像素级精细特征和频率级细化来提高感知质量，在模拟和真实数据集中取得了最优结果。