提出VRAE模型，高效修复交通监控中模糊噪点车牌图像。
VRAE: Vertical Residual Autoencoder for License Plate Denoising and Deblurring
- 采用垂直残差结构与输入感知辅助块，提升特征学习能力。
- 相比传统自编码器，PSNR提升20%，NMSE降低50%。
- 适合实时车牌识别系统，参数增长仅1%。
在真实交通监控场景中，恶劣天气、光线不足或高速运动导致车辆图像严重模糊和噪声干扰，尤其当车牌区域较小，会显著降低识别准确率。因此，快速实时地恢复这些退化图像成为提升识别性能的关键预处理步骤。本文提出一种用于交通监控图像增强的垂直残差自编码器（VRAE）架构。该方法引入辅助模块，在每个编码阶段注入输入感知特征，引导表征学习过程，相比传统自编码器更有效地保留整体信息。在包含可见车牌的车辆图像数据集上的实验表明，所提方法在相同深度下持续优于自编码器（AE）、生成对抗网络（GAN）和基于流的方法（FB）。相较于同深度的AE，其PSNR提升约20%，NMSE降低约50%，SSIM提升1%，同时仅增加约1%的参数量。
原文摘要 · Abstract (English)
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.
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