Recognition-Oriented Low-Light Image Enhancement Based on Global and Pixelwise Optimization

Seitaro Ono, Yuka Ogino, Takahiro Toizumi, Atsushi Ito, Masato Tsukada

2025

Abstract

In this paper, we propose a novel low-light image enhancement method aimed at improving the performance of recognition models. Despite recent advances in deep learning, the recognition of images under low-light conditions remains a challenge. Although existing low-light image enhancement methods have been developed to improve image visibility for human vision, they do not specifically focus on enhancing recognition model performance. Our proposed low-light image enhancement method consists of two key modules: the Global Enhance Module, which adjusts the overall brightness and color balance of the input image, and the Pixelwise Adjustment Module, which refines image features at the pixel level. These modules are trained to enhance input images to improve downstream recognition model performance effectively. Notably, the proposed method can be applied as a frontend filter to improve low-light recognition performance without requiring retraining of downstream recognition models. Experimental results demonstrate that our method improves the performance of pretrained recognition models under low-light conditions and its effectiveness.

Paper Citation

in Harvard Style

Ono S., Ogino Y., Toizumi T., Ito A. and Tsukada M. (2025). Recognition-Oriented Low-Light Image Enhancement Based on Global and Pixelwise Optimization. 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 222-232. DOI: 10.5220/0013316800003912

in Bibtex Style

@conference{visapp25,
author={Seitaro Ono and Yuka Ogino and Takahiro Toizumi and Atsushi Ito and Masato Tsukada},
title={Recognition-Oriented Low-Light Image Enhancement Based on Global and Pixelwise Optimization},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={222-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013316800003912},
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 - Recognition-Oriented Low-Light Image Enhancement Based on Global and Pixelwise Optimization
SN - 978-989-758-728-3
AU - Ono S.
AU - Ogino Y.
AU - Toizumi T.
AU - Ito A.
AU - Tsukada M.
PY - 2025
SP - 222
EP - 232
DO - 10.5220/0013316800003912
PB - SciTePress