Thermal Image Super-Resolution Using Real-ESRGAN for Human Detection

Vinícius H. G. Correa, Peter Funk, Nils Sundelius, Rickard Sohlberg, Mastura Ab Wahid, Alexandre C. B. Ramos

2025

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly crucial in Search and Rescue (SAR) operations due to their ability to enhance efficiency and reduce costs. Search and Rescue is a vital activity as it directly impacts the preservation of life and safety in critical situations, such as locating and rescuing individuals in perilous or remote environments. However, the effectiveness of these operations heavily depends on the quality of sensor data for accurate target detection. This study investigates the application of the Real Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN) algorithm to enhance the resolution and detail of infrared images captured by UAV sensors. By improving image quality through super-resolution, we then assess the performance of the YOLOv8 target detection algorithm on these enhanced images. Preliminary results indicate that Real-ESRGAN significantly improves the quality of low-resolution infrared data, even when using pre-trained models not specifically tailored to our dataset, this highlights a considerable potential of applying the algorithm in the preprocessing stages of images generated by UAVs for search and rescue operations.

Paper Citation

in Harvard Style

Correa V., Funk P., Sundelius N., Sohlberg R., Wahid M. and Ramos A. (2025). Thermal Image Super-Resolution Using Real-ESRGAN for Human Detection. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 247-254. DOI: 10.5220/0013078800003912

in Bibtex Style

@conference{visapp25,
author={Vinícius Correa and Peter Funk and Nils Sundelius and Rickard Sohlberg and Mastura Wahid and Alexandre Ramos},
title={Thermal Image Super-Resolution Using Real-ESRGAN for Human Detection},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={247-254},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013078800003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Thermal Image Super-Resolution Using Real-ESRGAN for Human Detection
SN - 978-989-758-728-3
AU - Correa V.
AU - Funk P.
AU - Sundelius N.
AU - Sohlberg R.
AU - Wahid M.
AU - Ramos A.
PY - 2025
SP - 247
EP - 254
DO - 10.5220/0013078800003912
PB - SciTePress