Abstract
In real-world traffic surveillance, vehicle images captured under adverse weather, poor lighting, or high-speed motion often suffer from severe noise and blur. Such degradations significantly reduce the accuracy of license plate recognition systems, especially when the plate occupies only a small region within the full vehicle image. Restoring these degraded images a fast realtime manner is thus a crucial pre-processing step to enhance recognition performance. In this work, we propose a Vertical Residual Autoencoder (VRAE) architecture designed for the image enhancement task in traffic surveillance. The method incorporates an enhancement strategy that employs an auxiliary block, which injects input-aware features at each encoding stage to guide the representation learning process, enabling better general information preservation throughout the network compared to conventional autoencoders. Experiments on a vehicle image dataset with visible license plates demonstrate that our method consistently outperforms Autoencoder (AE), Generative Adversarial Network (GAN), and Flow-Based (FB) approaches. Compared with AE at the same depth, it improves PSNR by about 20\%, reduces NMSE by around 50\%, and enhances SSIM by 1\%, while requiring only a marginal increase of roughly 1\% in parameters.
Abstract (translated)
在实际的交通监控中，恶劣天气、照明条件差或高速移动拍摄的车辆图像常常会受到严重的噪点和模糊影响。这些退化显著降低了车牌识别系统的准确性，尤其是在车牌仅占据整个车辆图像很小一部分的情况下。因此，在实时情况下恢复这些受损的图像是一个关键的预处理步骤，以提高识别性能。在本研究中，我们提出了一种垂直残差自编码器（VRAE）架构，专门用于交通监控中的图像增强任务。该方法采用了一种增强策略，利用辅助模块在每个编码阶段注入输入感知特征来指导表示学习过程，从而在整个网络中更好地保持一般信息的完整性和质量，相比传统自编码器具有优势。 实验在一个包含可见车牌的车辆图像数据集上进行，结果表明我们的方法在性能上持续优于自编码器（AE）、生成对抗网络（GAN）和基于流的方法（FB）。与相同深度的自编码器相比，它将PSNR提高了约20%，降低了NMSE约50%，并提升了SSIM 1%，而仅需增加大约1%的参数量。
URL
https://arxiv.org/abs/2509.08392
