License Plate Super-Resolution Using Diffusion Models

• URL: http://arxiv.org/abs/2309.12506v1
• Date: Thu, 21 Sep 2023 22:06:23 GMT
• Title: License Plate Super-Resolution Using Diffusion Models
• Authors: Sawsan AlHalawani, Bilel Benjdira, Adel Ammar, Anis Koubaa, Anas M. Ali
• Abstract summary: This study leverages the cutting-edge diffusion model, which has consistently outperformed other deep learning techniques in image restoration. The method achieves a 12.55% and 37.32% improvement in Peak Signal-to-Noise Ratio (PSNR) over SwinIR and ESRGAN, respectively.
• Score: 1.3499500088995464
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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