标题： 嵌入相似性引导的车牌超分辨率 显示英文标题

标题： Embedding Similarity Guided License Plate Super Resolution

摘要： 超分辨率（SR）技术在提升低分辨率图像质量方面起着关键作用，特别是在安全和监控等应用中，准确的车牌识别至关重要。 本研究提出了一种新的框架，结合基于像素的损失与嵌入相似性学习，以解决车牌超分辨率（LPSR）的独特挑战。 引入的像素和嵌入一致性损失（PECL）集成了孪生网络，并应用对比损失来强制嵌入相似性，以提高感知和结构保真度。 通过有效平衡像素级精度与嵌入级一致性，该框架实现了高分辨率（HR）和超分辨率（SR）车牌之间细粒度特征的优越对齐。 在CCPD数据集上的大量实验验证了所提出框架的有效性，在PSNR_RGB、PSNR_Y和光学字符识别（OCR）准确性方面均表现出一致的改进。 这些结果突显了嵌入相似性学习在极端超分辨率场景中提升感知质量和任务特定性能的潜力。 显示英文摘要

摘要： 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 dataset validate the efficacy of the proposed framework, demonstrating consistent improvements over state-of-the-art methods in terms of PSNR_RGB, PSNR_Y 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.