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Volume 11
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Article

Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications

by
Mohammad Hijji
1,*,†,
Abbas Khan
2,†,
Mohammed M. Alwakeel
1,
Rafika Harrabi
1,
Fahad Aradah
1,
Faouzi Alaya Cheikh
3,
Muhammad Sajjad
2,3,* and
Khan Muhammad
4,*
1
Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47711, Saudi Arabia
2
Digital Image Processing Laboratory, Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan
3
The Software, Data and Digital Ecosystems (SDDE) Research Group, Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway
4
Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Republic of Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work and are co-first authors.
Mathematics 2023, 11(4), 892; https://doi.org/10.3390/math11040892
Submission received: 15 December 2022 / Revised: 29 January 2023 / Accepted: 4 February 2023 / Published: 9 February 2023
(This article belongs to the Section E1: Mathematics and Computer Science)
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Abstract

Vehicle license plate images are often low resolution and blurry because of the large distance and relative motion between the vision sensor and vehicle, making license plate identification arduous. The extensive use of expensive, high-quality vision sensors is uneconomical in most cases; thus, images are initially captured and then translated from low resolution to high resolution. For this purpose, several traditional techniques such as bilinear, bicubic, super-resolution convolutional neural network, and super-resolution generative adversarial network (SRGAN) have been developed over time to upgrade low-quality images. However, most studies in this area pertain to the conversion of low-resolution images to super-resolution images, and little attention has been paid to motion de-blurring. This work extends SRGAN by adding an intelligent motion-deblurring method (termed SRGAN-LP), which helps to enhance the image resolution and remove motion blur from the given images. A comprehensive and new domain-specific dataset was developed to achieve improved results. Moreover, maintaining higher quantitative and qualitative results in comparison to the ground truth images, this study upscales the provided low-resolution image four times and removes the motion blur to a reasonable extent, making it suitable for surveillance applications.
Keywords: AI; SRGAN; image super-resolution; generator; discriminator; generative adversarial networks; motion blur; surveillance; SRGAN-LP; machine learning AI; SRGAN; image super-resolution; generator; discriminator; generative adversarial networks; motion blur; surveillance; SRGAN-LP; machine learning

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MDPI and ACS Style

Hijji, M.; Khan, A.; Alwakeel, M.M.; Harrabi, R.; Aradah, F.; Cheikh, F.A.; Sajjad, M.; Muhammad, K. Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications. Mathematics 2023, 11, 892. https://doi.org/10.3390/math11040892

AMA Style

Hijji M, Khan A, Alwakeel MM, Harrabi R, Aradah F, Cheikh FA, Sajjad M, Muhammad K. Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications. Mathematics. 2023; 11(4):892. https://doi.org/10.3390/math11040892

Chicago/Turabian Style

Hijji, Mohammad, Abbas Khan, Mohammed M. Alwakeel, Rafika Harrabi, Fahad Aradah, Faouzi Alaya Cheikh, Muhammad Sajjad, and Khan Muhammad. 2023. "Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications" Mathematics 11, no. 4: 892. https://doi.org/10.3390/math11040892

APA Style

Hijji, M., Khan, A., Alwakeel, M. M., Harrabi, R., Aradah, F., Cheikh, F. A., Sajjad, M., & Muhammad, K. (2023). Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications. Mathematics, 11(4), 892. https://doi.org/10.3390/math11040892

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