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.

翻译：近年来，基于深度神经网络的图像识别技术取得了显著进展。然而，在低光照条件下提升识别性能仍是一项重大挑战。本研究聚焦于增强低光照条件下识别模型的性能。我们提出了一种图像自适应可学习模块，该模块对输入图像实施适当的图像处理，并配备一个超参数预测器以预估模块中使用的最优参数。所提出的方法能够作为前端滤波器轻松集成，无需重新训练专为低光照条件设计的现有识别模型，即可在低光照条件下提升识别性能。实验结果表明，该方法有助于增强低光照环境下的图像识别性能。