License Plate Super-Resolution Using Diffusion Models
Sawsan AlHalawani Bilel Benjdira Adel Ammar Anis Koubaa Anas M. Ali
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.