Hyperspectral Image Super-Resolution Based on Spatial-Spectral-Frequency Multidimensional Features  

作　　者：Sifan Zheng Tao Zhang Haibing Yin Hao Hu Jian Jiang Chenggang Yan 

机构地区：[1]School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China [2]Lishui Institute of Hangzhou Dianzi University,Lishui 323000,China [3]China Mobile(Zhejiang)Innovation Research Institute Co.,Ltd.,Hangzhou 310018,China

出　　处：《Journal of Beijing Institute of Technology》2025年第1期28-41,共14页北京理工大学学报(英文版)

基　　金：supported by the Fundamental Research Funds for the Provincial Universities of Zhejiang (No.GK249909299001-036);National Key Research and Development Program of China (No. 2023YFB4502803);Zhejiang Provincial Natural Science Foundation of China (No.LDT23F01014F01)。

摘　　要：Due to the limitations of existing imaging hardware, obtaining high-resolution hyperspectral images is challenging. Hyperspectral image super-resolution(HSI SR) has been a very attractive research topic in computer vision, attracting the attention of many researchers. However, most HSI SR methods focus on the tradeoff between spatial resolution and spectral information, and cannot guarantee the efficient extraction of image information. In this paper, a multidimensional features network(MFNet) for HSI SR is proposed, which simultaneously learns and fuses the spatial,spectral, and frequency multidimensional features of HSI. Spatial features contain rich local details,spectral features contain the information and correlation between spectral bands, and frequency feature can reflect the global information of the image and can be used to obtain the global context of HSI. The fusion of the three features can better guide image super-resolution, to obtain higher-quality high-resolution hyperspectral images. In MFNet, we use the frequency feature extraction module(FFEM) to extract the frequency feature. On this basis, a multidimensional features extraction module(MFEM) is designed to learn and fuse multidimensional features. In addition, experimental results on two public datasets demonstrate that MFNet achieves state-of-the-art performance.

关 键 词：deep neural network hyperspectral image spatial feature spectral information frequency feature 

分 类 号：G63[文化科学—教育学]