Embedding Similarity Guided License Plate Super Resolution

TL;DR

This paper introduces a license plate super-resolution framework that combines pixel-based loss with embedding similarity learning, significantly improving perceptual quality and OCR accuracy in low-resolution images.

Contribution

It proposes a novel PECL framework integrating Siamese networks and contrastive loss for enhanced super-resolution of license plates, balancing pixel accuracy with embedding consistency.

Findings

Outperforms state-of-the-art methods in PSNR, SSIM, LPIPS, and OCR accuracy.

Demonstrates the effectiveness of embedding similarity learning in license plate super-resolution.

Validates the approach on CCPD and PKU datasets with consistent improvements.

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.