基于超分辨率重建的交通图像增强与拥堵预测研究

Research on Traffic Image Enhancement and Congestion Prediction Based on Super Resolution Reconstruction

摘要: 城市化进程的持续加速使得交通拥堵问题日益严峻，而实时且高清的道路监控图像构成了实施精准交通管理的关键基础。然而，现有的监控系统往往会受到硬件条件与环境干扰的制约，时常出现图像分辨率不足、光照不均以及运动模糊等问题，这些问题严重制约了后续交通参数的准确提取与分析工作。针对这一技术瓶颈，本研究提出了一种创新的技术框架，致力于从图像质量这一源头入手来提升交通感知能力。通过设计多尺度特征提取网络并优化残差生成式重建模型，本研究构建了一个高效且鲁棒的超分辨率重建核心架构。在此架构基础之上，进一步集成了非均匀光照补偿算法以及针对性的运动模糊抑制与边缘锐化处理流程，从而形成了一套完整的交通图像增强方案。将增强后所获得的高清图像序列与多源交通数据进行融合，并引入时空图神经网络对区域内的流量演化模式开展深度建模，由此构建起一个基于高质量视觉信息的动态拥堵推演与预测框架。在构建的真实场景数据集上所开展的验证实验表明，所提出的方法在图像重建质量与拥堵预测准确性方面，相较于所选取的基线方法(包括SRCNN、ESRGAN及基于图神经网络的预测模型)展现出明显的性能优势。本研究为解决复杂城市环境下的实时交通监控与预警任务提供了新的思路与可靠的技术路径，其成果可以直接嵌入到现有的智慧交通平台当中，为提升城市交通系统的运行效率与智能化水平提供有力的支撑。

Abstract: The accelerating urbanization process has led to increasingly severe traffic congestion issues. Real-time, high-definition road surveillance images are a crucial foundation for implementing precise traffic management. However, existing surveillance systems are often limited by hardware constraints and environmental interference, frequently suffering from problems such as insufficient image resolution, uneven illumination, and motion blur, which severely hinder the accurate extraction and analysis of subsequent traffic parameters. To address this bottleneck, this study proposes an innovative technical framework dedicated to enhancing traffic perception capabilities starting from the source of image quality. By designing a multi-scale feature extraction network and optimizing a residual generative reconstruction model, an efficient and robust core architecture for super-resolution reconstruction is constructed. Building upon this architecture, a non-uniform illumination compensation algorithm and a targeted motion blur suppression and edge sharpening processing pipeline are further integrated to form a comprehensive traffic image enhancement solution. The enhanced high-definition image sequences are fused with multi-source traffic data, and a spatio-temporal graph neural network is introduced to deeply model the traffic flow evolution patterns within the region, thereby establishing a dynamic congestion deduction and prediction framework based on high-quality visual information. Validation experiments conducted on the constructed real-world dataset indicate that the proposed method achieves improved performance in both image reconstruction quality and congestion prediction accuracy compared with the selected baseline methods, including SRCNN, ESRGAN, and graph neural network-based prediction models. This research provides new insights and a reliable technical pathway for addressing real-time traffic monitoring and early warning in complex urban environments. Its outcomes can be directly embedded into existing intelligent transportation platforms, offering strong support for improving the operational efficiency and intelligence level of urban traffic systems.