  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.
