University of Isfahan & Iranian Society of Cryptology Journal of Computing and Security 2322-4460 13 2 2026 07 01 Enhancing Low-Resolution Persian License Plates via Diffusion Models 63 74 30687 10.22108/jcs.2026.145412.1171 EN Mahsham Kushki Department of Telecommunications and Electronics Engineering, Faculty of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran. Esmat Rashedi Department of Telecommunications and Electronics Engineering, Faculty of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran. Elham Shabaninia Department of Applied Mathematics, Faculty of Modern Sciences and Technologies, Graduate University of Advanced Technology, Kerman, Iran. Mehdi Kamandar Department of Telecommunications and Electronics Engineering, Faculty of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran. Journal Article 2025 05 26 With the rapid growth of urbanization and the increasing number of vehicles, automatic license plate recognition systems have become an essential component of traffic monitoring and management. However, the accuracy of these systems decreases when the captured plate images have low resolution or are affected by environmental noise. This study investigates the use of diffusion models to enhance the resolution of Iranian license plate images under such challenging conditions. In the proposed framework, a diffusion-based denoising process progressively reconstructs and refines image details iteratively. Experimental evaluations demonstrate that the proposed method outperforms well-known approaches such as Bicubic interpolation and deep learning-based super-resolution models, including SRGAN, Real-ESRGAN, BSRGAN, and SwinIR, in terms of perceptual quality and objective metrics. Specifically, the model achieves a PSNR of 31.2458 and an SSIM of 0.8621. Moreover, OCR results indicate that the reconstructed images produced by the diffusion model lead to better character readability and enhanced recognition accuracy. https://jcomsec.ui.ac.ir/article_30687_afe1599c895db8e7372a85674e7dd7fe.pdf