Abstract
License plate recognition (LPR) is important for traffic law enforcement, crime investigation, and surveillance. However, license plate areas in dash cam images often suffer from low resolution, motion blur, and glare, which make accurate recognition challenging. Existing generative models that rely on pretrained priors cannot reliably restore such poor-quality images, frequently introducing severe artifacts and distortions. To address this issue, we propose a novel multi-frame license plate restoration and recognition framework, MF-LPR$^2$, which addresses ambiguities in poor-quality images by aligning and aggregating neighboring frames instead of relying on pretrained knowledge. To achieve accurate frame alignment, we employ a state-of-the-art optical flow estimator in conjunction with carefully designed algorithms that detect and correct erroneous optical flow estimations by leveraging the spatio-temporal consistency inherent in license plate image sequences. Our approach enhances both image quality and recognition accuracy while preserving the evidential content of the input images. In addition, we constructed a novel Realistic LPR (RLPR) dataset to evaluate MF-LPR$^2$. The RLPR dataset contains 200 pairs of low-quality license plate image sequences and high-quality pseudo ground-truth images, reflecting the complexities of real-world scenarios. In experiments, MF-LPR$^2$ outperformed eight recent restoration models in terms of PSNR, SSIM, and LPIPS by significant margins. In recognition, MF-LPR$^2$ achieved an accuracy of 86.44%, outperforming both the best single-frame LPR (14.04%) and the multi-frame LPR (82.55%) among the eleven baseline models. The results of ablation studies confirm that our filtering and refinement algorithms significantly contribute to these improvements.
Abstract (translated)
车牌识别（LPR）对于交通执法、犯罪调查和监控至关重要。然而，行车记录仪图像中的车牌区域常常分辨率低、模糊不清且有眩光，这使得准确识别变得困难。现有的依赖于预训练先验的生成模型无法可靠地恢复这些质量较差的图像，经常引入严重的伪影和失真。为解决这一问题，我们提出了一种新颖的多帧车牌修复与识别框架——MF-LPR$^2$，该框架通过将相邻帧对齐并聚合而不是依赖于预训练知识来处理低质量图像中的模糊性。为了实现精确的帧对齐，我们采用最先进的光流估计器，并结合精心设计的算法利用车牌图像序列中固有的时空一致性检测和校正错误的光流估计。我们的方法在增强图像质量和识别准确度的同时保留了输入图像的证据内容。 此外，为评估MF-LPR$^2$，我们构建了一个新颖的真实车牌识别（RLPR）数据集。该RLPR数据集包含200对低质量车牌图像序列和高质量伪真实标签图像，反映了现实场景中的复杂性。在实验中，MF-LPR$^2$以显著的性能差距，在PSNR、SSIM和LPIPS等指标上超越了八种最新的修复模型。识别方面，MF-LPR$^2$达到了86.44%的准确率，优于十一种基线模型中的最佳单帧车牌识别（14.04%）和多帧车牌识别（82.55%）。消融研究结果确认我们的过滤和细化算法对此性能改进有显著贡献。
URL
https://arxiv.org/abs/2508.14797
