Duan Puhong, Shan Tianci, Kang Xudong, Li Shutao
IEEE Trans Neural Netw Learn Syst. 2025 Jul;36(7):12338-12348. doi: 10.1109/TNNLS.2024.3481060.
Spectral super-resolution aims to reconstruct a hyperspectral image (HSI) from its corresponding RGB image, which has drawn much more attention in remote sensing field. Recent advances in the application of deep learning models for spectral super-resolution have demonstrated great potential. However, these methods only work in spectral-spatial domain while rarely explore the potential property in the frequency domain. In this work, we first attempt to address spectral super-resolution in the frequency domain. To well merge the frequency information into the super-resolution network, a spectral-spatial-frequency domain fusion network (SSFDF) is designed, which consists of three key parts: frequency-domain feature learning, spectral-spatial domain feature learning, and feature fusion module. In more detail, a frequency-domain feature learning network is first exploited to dig the frequency-domain information of the input data. Then, a symmetric convolutional neural network (CNN) is developed to acquire the spectral-spatial features of the input data, where a parameter-sharing strategy is utilized to reduce network parameters. Finally, a feature fusion module is proposed to reconstruct HSI. Comprehensive experiments on several datasets reveal that our method can attain state-of-the-art reconstruction result with respect to other spectral super-resolution techniques.
光谱超分辨率旨在从相应的RGB图像重建高光谱图像（HSI），这在遥感领域引起了更多关注。深度学习模型在光谱超分辨率应用方面的最新进展已显示出巨大潜力。然而，这些方法仅在光谱-空间域中起作用，很少探索频域中的潜在特性。在这项工作中，我们首次尝试在频域中解决光谱超分辨率问题。为了将频率信息很好地融合到超分辨率网络中，设计了一个光谱-空间-频域融合网络（SSFDF），它由三个关键部分组成：频域特征学习、光谱-空间域特征学习和特征融合模块。更详细地说，首先利用频域特征学习网络挖掘输入数据的频域信息。然后，开发一个对称卷积神经网络（CNN）来获取输入数据的光谱-空间特征，其中采用参数共享策略来减少网络参数。最后，提出一个特征融合模块来重建高光谱图像。在几个数据集上进行的综合实验表明，相对于其他光谱超分辨率技术，我们的方法可以获得最优的重建结果。
