Improving Low-Light Image Recognition Performance Based on Image-adaptive Learnable Module
This addresses a domain-specific challenge for computer vision applications in low-light environments, but it is incremental as it builds on existing recognition models.
The paper tackles the problem of improving image recognition performance under low-light conditions by proposing an image-adaptive learnable module with a hyperparameter predictor, which enhances performance without retraining existing models.
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.