用嵌入相似性提升车牌超分辨，让识别更准。
Embedding Similarity Guided License Plate Super Resolution
- 结合像素损失与嵌入相似性学习，提升细节还原。
- 在CCPD和PKU数据集上PSNR、SSIM、OCR准确率均领先。
- 适合需要高精度车牌识别的安防场景。
超分辨率技术对提升低分辨率图像质量至关重要，尤其在安全监控中，准确识别车牌尤为关键。本文提出一种新框架，融合基于像素的损失与嵌入相似性学习，以应对车牌超分辨（LPSR）的独特挑战。引入像素与嵌入一致性损失（PECL），采用孪生网络并应用对比损失，强制嵌入相似性，从而提升感知与结构保真度。通过有效平衡像素级精度与嵌入级一致性，该框架显著改善了高分辨率（HR）与超分辨（SR）车牌间的细粒度特征对齐。在CCPD和PKU数据集上的大量实验验证了该框架的有效性，在PSNR、SSIM、LPIPS及光学字符识别（OCR）准确率方面持续优于现有最先进方法。结果表明，嵌入相似性学习可显著提升极端超分辨场景下的感知质量与任务性能。
原文摘要 · Abstract (English)
Super-resolution (SR) techniques play a pivotal role in enhancing the quality of low-resolution images, particularly for applications such as security and surveillance, where accurate license plate recognition is crucial. This study proposes a novel framework that combines pixel-based loss with embedding similarity learning to address the unique challenges of license plate super-resolution (LPSR). The introduced pixel and embedding consistency loss (PECL) integrates a Siamese network and applies contrastive loss to force embedding similarities to improve perceptual and structural fidelity. By effectively balancing pixel-wise accuracy with embedding-level consistency, the framework achieves superior alignment of fine-grained features between high-resolution (HR) and super-resolved (SR) license plates. Extensive experiments on the CCPD and PKU dataset validate the efficacy of the proposed framework, demonstrating consistent improvements over state-of-the-art methods in terms of PSNR, SSIM, LPIPS, and optical character recognition (OCR) accuracy. These results highlight the potential of embedding similarity learning to advance both perceptual quality and task-specific performance in extreme super-resolution scenarios.
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