用字符级引导修复模糊车牌，提升识别准确率。
CharDiff-LP: A Diffusion Model with Character-Level Guidance for License Plate Image Restoration
- 引入字符级先验和区域掩码注意力模块，精准引导修复。
- 在Roboflow-LP数据集上字符错误率降低28.3%。
- 适合交通监控、司法取证等需要高精度车牌识别场景。
车牌图像修复不仅是车牌识别的预处理步骤，还能增强证据价值、提升视觉清晰度并促进图像的广泛使用。本文提出一种基于扩散模型的新型框架CharDiff-LP，通过外部分割与专为低质量车牌设计的OCR模块提取细粒度字符级先验，实现对真实条件下严重退化的车牌图像的有效修复与识别。为实现精确聚焦的引导，引入新颖的区域掩码字符引导注意力（CHARM）模块，确保每个字符的指导仅限于其所在区域，避免跨区域干扰。实验表明，CharDiff-LP在修复质量与识别准确率上显著优于基线模型，在Roboflow-LP数据集上相较最优基线实现了28.3%的相对字符错误率（CER）降低。
原文摘要 · Abstract (English)
License plate image restoration is important not only as a preprocessing step for license plate recognition but also for enhancing evidential value, improving visual clarity, and enabling broader reuse of license plate images. We propose a novel diffusion-based framework with character-level guidance, CharDiff-LP, which effectively restores and recognizes severely degraded license plate images captured under realistic conditions. CharDiff-LP leverages fine-grained character-level priors extracted through external segmentation and Optical Character Recognition (OCR) modules tailored for low-quality license plate images. For precise and focused guidance, CharDiff-LP incorporates a novel Character-guided Attention through Region-wise Masking (CHARM) module, which ensures that each character's guidance is restricted to its own region, thereby avoiding interference with other regions. In experiments, CharDiff-LP significantly outperformed baseline restoration models in both restoration quality and recognition accuracy, achieving a 28.3% relative reduction in character error rate (CER) on the Roboflow-LP dataset compared with the best-performing baseline.
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