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Kushki, Mahsham, Rashedi, Esmat, Shabaninia, Elham, Kamandar, Mehdi. (1405). Enhancing Low-Resolution Persian License Plates via Diffusion Models. , 13(2), 63-74. doi: 10.22108/jcs.2026.145412.1171
Mahsham Kushki; Esmat Rashedi; Elham Shabaninia; Mehdi Kamandar. "Enhancing Low-Resolution Persian License Plates via Diffusion Models". , 13, 2, 1405, 63-74. doi: 10.22108/jcs.2026.145412.1171
Kushki, Mahsham, Rashedi, Esmat, Shabaninia, Elham, Kamandar, Mehdi. (1405). 'Enhancing Low-Resolution Persian License Plates via Diffusion Models', , 13(2), pp. 63-74. doi: 10.22108/jcs.2026.145412.1171
Kushki, Mahsham, Rashedi, Esmat, Shabaninia, Elham, Kamandar, Mehdi. Enhancing Low-Resolution Persian License Plates via Diffusion Models. , 1405; 13(2): 63-74. doi: 10.22108/jcs.2026.145412.1171

Enhancing Low-Resolution Persian License Plates via Diffusion Models

Journal of Computing and Security
دوره 13، شماره 2، مهر 2026، صفحه 63-74    اصل مقاله (1.27 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22108/jcs.2026.145412.1171
نویسندگان
Mahsham Kushki1؛ Esmat Rashedi* 1؛ Elham Shabaninia2؛ Mehdi Kamandar1
1Department of Telecommunications and Electronics Engineering, Faculty of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran.
2Department of Applied Mathematics, Faculty of Modern Sciences and Technologies, Graduate University of Advanced Technology, Kerman, Iran.
چکیده
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.
کلیدواژه‌ها
Super-Resolution؛ Deep Learning؛ Diffusion Models؛ Persian License Plate Images
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