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• W4382138659 abstract "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, leading to negative effects on traffic supervision and management. 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, especially when the image captured by low-cost traffic cameras is too degraded to meet the prerequisites for single-frame image enhancement. 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 the proposed method effectively improves the visibility of night surveillance images and significantly facilitates object detection in nighttime traffic scenes. Compared with the state-of-the-art baselines, our method has significant advantages in enhancing details and making colors more natural for human observation." @default.
• W4382138659 created "2023-06-27" @default.
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• W4382138659 date "2023-01-01" @default.
• W4382138659 modified "2023-09-26" @default.
• W4382138659 title "Multi-Exposure Fusion With Guidance Information: Night Color Image Enhancement for Roadside Units" @default.
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• W4382138659 doi "https://doi.org/10.1109/access.2023.3289595" @default.
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