In surveillance, accurately recognizing license plates is hindered by their often low quality and small dimensions, compromising recognition precision. Despite advancements in AI-based image super-resolution, methods like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) still fall short in enhancing license plate images. This study leverages the cutting-edge diffusion model, which has consistently outperformed other deep learning techniques in image restoration. By training this model using a curated dataset of Saudi license plates, both in low and high resolutions, we discovered the diffusion model's superior efficacy. The method achieves a 12.55\% and 37.32% improvement in Peak Signal-to-Noise Ratio (PSNR) over SwinIR and ESRGAN, respectively. Moreover, our method surpasses these techniques in terms of Structural Similarity Index (SSIM), registering a 4.89% and 17.66% improvement over SwinIR and ESRGAN, respectively. Furthermore, 92% of human evaluators preferred our images over those from other algorithms. In essence, this research presents a pioneering solution for license plate super-resolution, with tangible potential for surveillance systems.

翻译：在监控场景中，车牌识别常因图像质量低、尺寸小而导致识别精度受限。尽管基于人工智能的图像超分辨率技术（如卷积神经网络和生成对抗网络）已取得进展，但在增强车牌图像方面仍存在不足。本研究采用前沿的扩散模型——该模型在图像修复领域持续优于其他深度学习技术。通过使用沙特阿拉伯车牌的高低分辨率配对数据集训练该模型，我们发现扩散模型具有更优效能。该方法在峰值信噪比指标上较SwinIR和ESRGAN分别提升12.55%和37.32%；同时，在结构相似性指数上分别实现4.89%和17.66%的提升。此外，92%的人类评估者更倾向于选择本方法生成的图像。本质上，这项研究为车牌超分辨率提供了开创性解决方案，对监控系统具有实际应用潜力。