Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction

TL;DR

This paper introduces a deep unfolding network that enhances hyperspectral image super-resolution by automatically correcting exposure differences, enabling high-quality fusion under challenging lighting conditions.

Contribution

The proposed UHSR-AEC method uniquely integrates automatic exposure correction with hyperspectral super-resolution using deep unfolding, addressing extreme lighting scenarios.

Findings

Outperforms benchmark methods in quality of super-resolved images

Effectively handles imbalanced exposure conditions

Demonstrates superior texture and feature preservation

Abstract

In recent years, the fusion of high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI) has been recognized as an effective method for HSI super-resolution (HSI-SR). However, both HSI and MSI may be acquired under extreme conditions such as night or poorly illuminating scenarios, which may cause different exposure levels, thereby seriously downgrading the yielded HSISR. In contrast to most existing methods based on respective low-light enhancements (LLIE) of MSI and HSI followed by their fusion, a deep Unfolding HSI Super-Resolution with Automatic Exposure Correction (UHSR-AEC) is proposed, that can effectively generate a high-quality fused HSI-SR (in texture and features) even under very imbalanced exposures, thanks to the correlation between LLIE and HSI-SR taken into account. Extensive experiments are provided to demonstrate the state-of-the-art overall performance of the proposed UHSR-AEC, including comparison with some benchmark peer methods.