期刊
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
卷 -, 期 -, 页码 17521-17530出版社
IEEE COMPUTER SOC
DOI: 10.1109/CVPR52688.2022.01702
关键词
-
资金
- NSFC fund [61831014]
- Shenzhen Science and Technology Project [ZDYBH201900000002, CJGJZD20200617102601004]
- Westlake Foundation [2021B1501-2]
- Lochn Optics
This paper proposes a high-resolution dual-domain learning network (HDNet) for the reconstruction of hyperspectral images (HSI). By combining HR spatial-spectral attention module and frequency domain learning (FDL), the network achieves good performance in preserving fine-grained features and maintaining frequency domain consistency.
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. So 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 experiments show that our method achieves SOTA performance on simulated and real HSI datasets. httpss: // github . com/Huxiaowan/HDNet
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