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Authors: Seitaro Ono 1 ; Yuka Ogino 2 ; Takahiro Toizumi 2 ; Atsushi Ito 2 and Masato Tsukada 1 ; 2

Affiliations: 1 University of Tsukuba, Ibaraki, Japan ; 2 NEC Corporation, Kanagawa, Japan

Keyword(s): Low-Light Image Enhancement, Image Recognition.

Abstract: In recent years, significant progress has been made in image recognition technology based on deep neural networks. However, improving recognition performance under low-light conditions remains a significant challenge. This study addresses the enhancement of recognition model performance in low-light conditions. We propose an image-adaptive learnable module which apply appropriate image processing on input images and a hyperparameter predictor to forecast optimal parameters used in the module. Our proposed approach allows for the enhancement of recognition performance under low-light conditions by easily integrating as a front-end filter without the need to retrain existing recognition models designed for low-light conditions. Through experiments, our proposed method demonstrates its contribution to enhancing image recognition performance under low-light conditions.

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Paper citation in several formats:
Ono, S.; Ogino, Y.; Toizumi, T.; Ito, A. and Tsukada, M. (2024). Improving Low-Light Image Recognition Performance Based on Image-Adaptive Learnable Module. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 721-728. DOI: 10.5220/0012459700003660

@conference{visapp24,
author={Seitaro Ono. and Yuka Ogino. and Takahiro Toizumi. and Atsushi Ito. and Masato Tsukada.},
title={Improving Low-Light Image Recognition Performance Based on Image-Adaptive Learnable Module},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={721-728},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012459700003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Improving Low-Light Image Recognition Performance Based on Image-Adaptive Learnable Module
SN - 978-989-758-679-8
IS - 2184-4321
AU - Ono, S.
AU - Ogino, Y.
AU - Toizumi, T.
AU - Ito, A.
AU - Tsukada, M.
PY - 2024
SP - 721
EP - 728
DO - 10.5220/0012459700003660
PB - SciTePress

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