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 https://github.com/caiyuanhao1998/MST
翻译:深层学习的迅速发展为超光谱图像的端到端重建提供了更好的解决办法。然而,现有的基于学习的方法有两个主要缺陷。首先,自省网络通常牺牲内部分辨率,以平衡模型性能与复杂度之间的平衡,失去精细高分辨率特征。第二,即使侧重于空间光谱域学习的优化与理想解决方案相契合,重建后的高光谱图像和真相之间仍然存在着显著的视觉差异。因此,我们提议为HSI重建建立一个高分辨率双向学习网络(HDNet) 。一方面,拟议的HR空间光谱关注模块及其高效特征融合提供了连续和精细的像素级特征。另一方面,为HSI重建引入频域学习(SDL),以缩小频域差异。动态的FDL监督促使重建微光频率和补偿因像素级损失造成的过度平流和扭曲的模型。在我们的HDI-光谱-光谱网络中,HR-光谱关注和频率级改进模块提供了连续和精细的像级级功能-级模型。在SHDIS-ST-MSAS-SAS-Simal ASimal ASimal ASemex ASemex ASex ASemalal ASemlim化模型中,将实现实际质量和质量评估。