GWNet: A Lightweight Model for Low-Light Image Enhancement Using Gamma Correction and Wavelet Transform

Ming-Yu Kuo, Sheng-De Wang

2025

Abstract

Low-light image enhancement is essential for improving visual quality in various applications. We introduce GammaWaveletNet (GWNet), a novel approach that is composed of a gamma correction module and a wavelet network. The wavelet network is a sequential model with L subnetwork and H subnetwork. Both subnetworks use a U-Net architecture with Spatial Wavelet Interaction (SWI) component that is making use of wavelet transforms and convolution layers. The L subnetwork handles low-frequency components, while the H subnetwork refines high-frequency details, effectively combining spatial and frequency domain information for superior performance. Experimental results across datasets of different sizes demonstrate that GWNet achieves performance on par with state-of-the-art methods in terms of Peak Signal-to-Noise Ratio and Structural Similarity Index. Notably, the incorporation of wavelet transforms in GWNet leads to remarkable computational efficiency, reducing GFLOPs by approximately 75% and parameters by 40%, highlighting its potential for real-time applications on resource-constrained devices.

Paper Citation

in Harvard Style

Kuo M. and Wang S. (2025). GWNet: A Lightweight Model for Low-Light Image Enhancement Using Gamma Correction and Wavelet Transform. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 264-274. DOI: 10.5220/0013148800003890

in Bibtex Style

@conference{icaart25,
author={Ming-Yu Kuo and Sheng-De Wang},
title={GWNet: A Lightweight Model for Low-Light Image Enhancement Using Gamma Correction and Wavelet Transform},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={264-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013148800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - GWNet: A Lightweight Model for Low-Light Image Enhancement Using Gamma Correction and Wavelet Transform
SN - 978-989-758-737-5
AU - Kuo M.
AU - Wang S.
PY - 2025
SP - 264
EP - 274
DO - 10.5220/0013148800003890
PB - SciTePress