Multi-Exposure Fusion With Guidance Information: Night Color Image Enhancement for Roadside Units

The computer vision-based roadside occupation surveillance system is a key infrastructure component of Cooperative Intelligent Transport Systems. However, traffic images captured under low-light conditions suffer from low visibility and unexpected noise. Despite the great progress achieved in recent years, the existing night image enhancement algorithms often suffer from color deviation, ghosting, and overexposure problems in practical traffic applications. Thus, we present a novel night color image enhancement approach to overcome this issue by combining multi-sensor fusion and pseudo-multi-exposure fusion techniques. Unlike the traditional exposure adjustment-based approaches, we performed a novel bidirectional region segmentation-based inverse tone mapping operator to generate pseudo-multi-exposure sequences from day and night image pairs. Meanwhile, to solve the problem that moving objects are diluted after fusion, a partial differential equation (PDE)-based luminance stretching is applied to the moving areas to guarantee that the enhanced image always highlights the traffic targets. Instead of image feature-based methods for moving object detection, we generate more accurate moving regions by fusing data from the radar and camera sensors. Finally, a pyramid-based fusion method with an improved weight function is conducted to generate high-quality traffic images. The proposed method and five state-of-the-art methods are evaluated on randomly selected images from the Rope-3D database and nighttime images captured by an Intelligent Roadside Surveillance System. The experimental results demonstrate that our method has significant advantages in enhancing details and making colors more natural for human observation.