Improving Low-Light Image Recognition Performance Based on
  Image-adaptive Learnable Module
Seitaro Ono Yuka Ogino Takahiro Toizumi Atsushi Ito Masato Tsukada
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.