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

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.