Embedding Similarity Guided License Plate Super Resolution

• URL: http://arxiv.org/abs/2501.01483v2
• Date: Wed, 08 Jan 2025 14:29:10 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 to address the 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.
• Score: 4.315486098289241
• License:
• 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 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.

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