Single hyperspectral image super-resolution using a progressive upsampling deep prior network

Hyperspectral image super-resolution (SR) aims to enhance the spectral and spatial resolution of remote sensing images, enabling more accurate and detailed analysis of ground objects. However, hyperspectral images have high dimensional characteristics and complex spectral patterns. As a result, it is critical to effectively leverage the spatial non-local self-similarity and spectral correlation within hyperspectral images. To address this, we have proposed a novel single hyperspectral image SR method based on a progressive upsampling deep prior network. Specifically, we introduced the spatial-spectral attention fusion unit (S2AF) based on residual connections, in order to extract spatial and spectral information from hyperspectral images. Then we developed the group convolutional upsampling (GCU) to efficiently utilize the spatial and spectral prior information inherent in hyperspectral images. To address the challenges posed by the high dimensionality of hyperspectral images and limited training dataset, we implemented a parameter-sharing grouped convolutional upsampling framework within the GCU to ensure model stability and enhance performance. The experimental results on three benchmark datasets demonstrated that the proposed single hyperspectral image SR using a progressive upsampling deep prior network (PUDPN) method effectively improves the reconstruction quality of hyperspectral images and achieves promising performance.