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.
@article{arxiv.2401.06438,
title = {Improving Low-Light Image Recognition Performance Based on Image-adaptive Learnable Module},
author = {Seitaro Ono and Yuka Ogino and Takahiro Toizumi and Atsushi Ito and Masato Tsukada},
journal= {arXiv preprint arXiv:2401.06438},
year = {2025}
}