Improving Low-Light Image Recognition Performance Based on Image-adaptive Learnable Module

• URL: http://arxiv.org/abs/2401.06438v2
• Date: Wed, 08 Jan 2025 01:11:07 GMT
• Title: Improving Low-Light Image Recognition Performance Based on Image-adaptive Learnable Module
• Authors: Seitaro Ono, Yuka Ogino, Takahiro Toizumi, Atsushi Ito, Masato Tsukada,
• Abstract summary: This study addresses the enhancement of recognition model performance in low-light conditions.<n>We propose an image-adaptive learnable module which apply appropriate image processing on input images.<n>Our proposed approach allows for the enhancement of recognition performance under low-light conditions by easily integrating as a front-end filter.
• Score: 0.4951599300340954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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