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

Mohammad Hijji (), Abbas Khan, Mohammed M. Alwakeel, Rafika Harrabi, Fahad Aradah, Faouzi Alaya Cheikh, Muhammad Sajjad () and Muhammad Khan
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Mohammad Hijji: Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47711, Saudi Arabia
Abbas Khan: Digital Image Processing Laboratory, Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan
Mohammed M. Alwakeel: Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47711, Saudi Arabia
Rafika Harrabi: Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47711, Saudi Arabia
Fahad Aradah: Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47711, Saudi Arabia
Faouzi Alaya Cheikh: The Software, Data and Digital Ecosystems (SDDE) Research Group, Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway
Muhammad Sajjad: Digital Image Processing Laboratory, Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan

Mathematics, 2023, vol. 11, issue 4, 1-13

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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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