Polarization-Aware Low-Light Image Enhancement for Nighttime Intelligent Vehicles

Yang Lu, Jiandong Tian, Yiming Su, Jiawei Liu, Chunhui Hao, Chenghai Yue, Xin Yang

Abstract: Polarization-basedenvironmental perception is essential for intelligent vehicles. However, images taken in low-light conditions often have poor visibility. Deterioration of visual quality will decrease the accuracy of intelligent vehicle applications based on polarization, resulting in a decreased perception range and increased navigation risk. To suppress the effects of real-world nighttime scenes for intelligent vehicles, we propose a retinex-based polarization-aware low-light image enhancement network (RPLENet) that can enhance the polarization and color information. We prove that the reflectance layer obtained from the retinex decomposition of the unpolarized component $I_{un}$ is more accurate than the reflectance layer from any polarizer angle intensity image. Therefore, we perform retinex decomposition on $I_{un}$ to obtain a more precise reflectance layer instead of decomposing the reflectance layer in the polarizer angle intensity image. Our method achieves state-of-the-art performance by conducting extensive experiments on both synthetic and real-world datasets. We also collected polarization datasets from real-world nighttime road scenes to evaluate the performance of RPLENet. Our method can effectively improve the perception of polarization-based features in low-light conditions. This is of great value for the environmental robustness of visual perception systems and transportation safety.