Embedding Similarity Guided License Plate Super Resolution

• URL: http://arxiv.org/abs/2501.01483v3
• Date: Wed, 03 Sep 2025 07:43:47 GMT
• Title: Embedding Similarity Guided License Plate Super Resolution
• Authors: Abderrezzaq Sendjasni, Mohamed-Chaker Larabi,
• Abstract summary: This study proposes a novel framework that combines pixel-based loss with embedding similarity learning.<n>Experiments on the CCPD and PKU dataset validate the efficacy of the proposed framework.
• Score: 3.16770435670322
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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