| Autor: |
AlHalawani, Sawsan, Benjdira, Bilel, Ammar, Adel, Koubaa, Anis, Ali, Anas M. |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Electronics, Vol. 13, No. 13, Article 2670, 2024 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.3390/electronics13132670 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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