Hyperspectral Image Super-Resolution Using Multi-scale Feature Pyramid Network

Abstract

Hyperspectral (HS) images are captured with rich spectral information, which have been proved to be useful in many real-world applications, such as earth observation. Due to the limitations of HS cameras, it is difficult to obtain HS images with high-resolution (HR). Recent advances in deep learning (DL) for single image super-resolution (SISR) task provide a powerful tool for restoring high-frequency details from low-resolution (LR) input image. Inspired by this progress, in this paper, we present a novel DL-based model for single HS image super-resolution in which a feature pyramid block is designed to extract multi-scale features of the input HS image. Our method does not need auxiliary inputs which further extends the application scenes. Experiment results show that our method outperforms state-of-the-arts on both objective quality indices and subjective visual results.