Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization

TL;DR

This paper introduces a hybrid super-resolution framework for hyperspectral images that combines learning-based and model-based methods, achieving superior spatial and spectral quality over existing approaches.

Contribution

It proposes a novel integration of RGB fusion with TV-TV minimization, ensuring measurement consistency and improved image quality in hyperspectral super-resolution.

Findings

Outperforms current leading methods in spatial resolution

Enhances spectral fidelity in hyperspectral images

Ensures measurement consistency in reconstructed images

Abstract

Hyperspectral (HS) images contain detailed spectral information that has proven crucial in applications like remote sensing, surveillance, and astronomy. However, because of hardware limitations of HS cameras, the captured images have low spatial resolution. To improve them, the low-resolution hyperspectral images are fused with conventional high-resolution RGB images via a technique known as fusion based HS image super-resolution. Currently, the best performance in this task is achieved by deep learning (DL) methods. Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image. Conversely, model-based algorithms can typically guarantee such measurement consistency. Inspired by these observations, we propose a framework that integrates learning and model based methods. Experimental results show that our method produces images of superior spatial and spectral resolution compared to the current leading methods, whether model- or DL-based.