Super-resolution of car plate images using generative adversarial networks

Car plate recognition is used in traffic monitoring and control systems such as intelligent parking lot management, finding stolen vehicles, and automated highway toll. Car plate recognition consists of several stages of processing namely, car plate localization, extraction, and recognition which co...

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Main Author:	Tan, Kean Lai
Format:	Thesis
Language:	English
Published:	2018
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/79550/1/TanKeanLaiMFKE2018.pdf
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id	my-utm-ep.79550
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spelling	my-utm-ep.795502018-10-31T12:58:24Z Super-resolution of car plate images using generative adversarial networks 2018 Tan, Kean Lai TK Electrical engineering. Electronics Nuclear engineering Car plate recognition is used in traffic monitoring and control systems such as intelligent parking lot management, finding stolen vehicles, and automated highway toll. Car plate recognition consists of several stages of processing namely, car plate localization, extraction, and recognition which consists of Optical Character Recognition (OCR). However, in practice, Low-Resolution (LR) images or videos are widely used in surveillance systems. In low resolution surveillance systems, the car plate text is often illegible. Other than that, small car plate due to the distance and illumination cause the car plate recognition to fail as well. Super-Resolution (SR) techniques can be used to improve the car plate quality by processing a series of LR images into a single High-Resolution (HR) image. Today, the best upscaling algorithms cannot effectively reconstruct data that does not exist. Recovering the HR image from a single LR is still an ill-conditioned problem for SR. Previous methods always minimize the mean square loss in order to improve the peak signal to noise ratio(PSNR). However, minimizing the mean square loss leads to overly smoothed reconstructed image. In this project, Generative Adversarial Networks (GANs) based SR is proposed to reconstruct the LR images into HR images. Besides that, perceptual loss is proposed to solve the smoothing issue. The quality of the GAN based SR generated images will be compared to existing techniques such as bicubic, nearest and Super-Resolution Convolution Neural Network (SRCNN). The results show that the reconstructed images using GANs based SR achieve better results in term of perceptual quality compared to previous methods. 2018 Thesis http://eprints.utm.my/id/eprint/79550/ http://eprints.utm.my/id/eprint/79550/1/TanKeanLaiMFKE2018.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Tan, Kean Lai Super-resolution of car plate images using generative adversarial networks
description	Car plate recognition is used in traffic monitoring and control systems such as intelligent parking lot management, finding stolen vehicles, and automated highway toll. Car plate recognition consists of several stages of processing namely, car plate localization, extraction, and recognition which consists of Optical Character Recognition (OCR). However, in practice, Low-Resolution (LR) images or videos are widely used in surveillance systems. In low resolution surveillance systems, the car plate text is often illegible. Other than that, small car plate due to the distance and illumination cause the car plate recognition to fail as well. Super-Resolution (SR) techniques can be used to improve the car plate quality by processing a series of LR images into a single High-Resolution (HR) image. Today, the best upscaling algorithms cannot effectively reconstruct data that does not exist. Recovering the HR image from a single LR is still an ill-conditioned problem for SR. Previous methods always minimize the mean square loss in order to improve the peak signal to noise ratio(PSNR). However, minimizing the mean square loss leads to overly smoothed reconstructed image. In this project, Generative Adversarial Networks (GANs) based SR is proposed to reconstruct the LR images into HR images. Besides that, perceptual loss is proposed to solve the smoothing issue. The quality of the GAN based SR generated images will be compared to existing techniques such as bicubic, nearest and Super-Resolution Convolution Neural Network (SRCNN). The results show that the reconstructed images using GANs based SR achieve better results in term of perceptual quality compared to previous methods.
format	Thesis
qualification_level	Master's degree
author	Tan, Kean Lai
author_facet	Tan, Kean Lai
author_sort	Tan, Kean Lai
title	Super-resolution of car plate images using generative adversarial networks
title_short	Super-resolution of car plate images using generative adversarial networks
title_full	Super-resolution of car plate images using generative adversarial networks
title_fullStr	Super-resolution of car plate images using generative adversarial networks
title_full_unstemmed	Super-resolution of car plate images using generative adversarial networks
title_sort	super-resolution of car plate images using generative adversarial networks
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2018
url	http://eprints.utm.my/id/eprint/79550/1/TanKeanLaiMFKE2018.pdf
_version_	1747818253765312512

Super-resolution of car plate images using generative adversarial networks

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