Embedding Similarity Learning for Extreme License Plate Super-Resolution

Super-resolution (SR) techniques play a crucial role in enhancing the quality of low-resolution images, with significant applications in fields such as security and surveillance, where license plate recognition is critical. This paper focuses on optimizing the super-resolution of license plates using embedding similarity learning. We proposed a novel framework that integrates a Siamese network with a super-resolution model to guide the SR model into enhancing the perceptual quality of reconstructed license plates. By leveraging embedding similarity through Contrastive loss, our approach ensures that the super-resolved images are perceptually and structurally closer to the original ones. The experiments on a synthetic dataset demonstrated that the proposed method outperforms traditional techniques that rely solely on pixel-based loss functions such as MSE. The introduction of embedding similarity loss significantly improves the PSNR and LPIPS metrics, in addition to the optical characters recognition rate.