Arbitrary-Scale Hyperspectral Image Super-Resolution From a Fusion Perspective With Spatial Priors

High-resolution hyperspectral image (HR HSI) plays a crucial role in remote sensing applications. The single HSI super-resolution (SR) method aims to obtain an HR HSI in the spatial domain from its low-resolution (LR) counterpart. Although it has been widely studied, the performance of the existing HSI SR method is still limited because the HSI data structure itself cannot provide sufficient spatial information for reconstruction, especially with a large SR factor. In this study, we cast single HSI SR as a task fusing LR HSI with its spectral response RGB image, from which the prevalent extra high-resolution RGB images can be introduced to provide sufficient and high-quality spatial prior information for HSI SR even with a large SR factor. Within this framework, we further propose an HSI arbitrary-scale SR method, which naturally incorporates such a spatial prior in both feature extraction and local implicit image function (LIIF). Extensive experiments on two benchmark remote sensing HSI datasets, showcasing the exceptional SR performance of our proposed method. The proposed SPG-ASSR method outperforms state-of-the-art (SOTA) approaches, demonstrating its effectiveness and practical applicability.