A New Image Enhancement and Super Resolution technique for license plate recognition

License Plate Recognition (LPR) is an important implemented application of Artificial Intelligence (AI) and deep learning in the past decades. However, due to the low image quality caused by the fast movement of vehicles and low-quality analogue cameras, many plate numbers cannot be recognised accur...

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Autores principales:	Abdelsalam Hamdi, Yee Kit Chan, Voon Chet Koo
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Image Super Resolution Image Enhancement LPR Science (General) Q1-390 Social sciences (General) H1-99
Acceso en línea:	https://doaj.org/article/6de844c58d7e4adb8067be9895a5ca05
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id	oai:doaj.org-article:6de844c58d7e4adb8067be9895a5ca05
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spelling	oai:doaj.org-article:6de844c58d7e4adb8067be9895a5ca052021-12-02T05:02:45ZA New Image Enhancement and Super Resolution technique for license plate recognition2405-844010.1016/j.heliyon.2021.e08341https://doaj.org/article/6de844c58d7e4adb8067be9895a5ca052021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405844021024440https://doaj.org/toc/2405-8440License Plate Recognition (LPR) is an important implemented application of Artificial Intelligence (AI) and deep learning in the past decades. However, due to the low image quality caused by the fast movement of vehicles and low-quality analogue cameras, many plate numbers cannot be recognised accurately by LPR models. To solve this issue, we propose a new deep learning architecture called D_GAN_ESR (Double Generative Adversarial Networks for Image Enhancement and Super Resolution) used for effective image denoising and super-resolution for license plate images. In this paper, we show the limitation of the existing networks for image enhancement and image super-resolution. Furthermore, a feature-based evaluation metric called Peak Signal to Noise Ratio Features (PSNR-F) is used to evaluate and compare performance between different methods. It is shown that the use of PSNR-F has a better performance indicator than the classical PSNR-pixel-to-pixel (PSNR-pixel) evaluation metric. The results show that using D_GAN_ESR to enhance the license plate images increases the LPR accuracy from 30% to 78% when blur images are used and increases the accuracy from 59% to 74.5% when low-quality images are used.Abdelsalam HamdiYee Kit ChanVoon Chet KooElsevierarticleImage Super ResolutionImage EnhancementLPRScience (General)Q1-390Social sciences (General)H1-99ENHeliyon, Vol 7, Iss 11, Pp e08341- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Image Super Resolution Image Enhancement LPR Science (General) Q1-390 Social sciences (General) H1-99
spellingShingle	Image Super Resolution Image Enhancement LPR Science (General) Q1-390 Social sciences (General) H1-99 Abdelsalam Hamdi Yee Kit Chan Voon Chet Koo A New Image Enhancement and Super Resolution technique for license plate recognition
description	License Plate Recognition (LPR) is an important implemented application of Artificial Intelligence (AI) and deep learning in the past decades. However, due to the low image quality caused by the fast movement of vehicles and low-quality analogue cameras, many plate numbers cannot be recognised accurately by LPR models. To solve this issue, we propose a new deep learning architecture called D_GAN_ESR (Double Generative Adversarial Networks for Image Enhancement and Super Resolution) used for effective image denoising and super-resolution for license plate images. In this paper, we show the limitation of the existing networks for image enhancement and image super-resolution. Furthermore, a feature-based evaluation metric called Peak Signal to Noise Ratio Features (PSNR-F) is used to evaluate and compare performance between different methods. It is shown that the use of PSNR-F has a better performance indicator than the classical PSNR-pixel-to-pixel (PSNR-pixel) evaluation metric. The results show that using D_GAN_ESR to enhance the license plate images increases the LPR accuracy from 30% to 78% when blur images are used and increases the accuracy from 59% to 74.5% when low-quality images are used.
format	article
author	Abdelsalam Hamdi Yee Kit Chan Voon Chet Koo
author_facet	Abdelsalam Hamdi Yee Kit Chan Voon Chet Koo
author_sort	Abdelsalam Hamdi
title	A New Image Enhancement and Super Resolution technique for license plate recognition
title_short	A New Image Enhancement and Super Resolution technique for license plate recognition
title_full	A New Image Enhancement and Super Resolution technique for license plate recognition
title_fullStr	A New Image Enhancement and Super Resolution technique for license plate recognition
title_full_unstemmed	A New Image Enhancement and Super Resolution technique for license plate recognition
title_sort	new image enhancement and super resolution technique for license plate recognition
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/6de844c58d7e4adb8067be9895a5ca05
work_keys_str_mv	AT abdelsalamhamdi anewimageenhancementandsuperresolutiontechniqueforlicenseplaterecognition AT yeekitchan anewimageenhancementandsuperresolutiontechniqueforlicenseplaterecognition AT voonchetkoo anewimageenhancementandsuperresolutiontechniqueforlicenseplaterecognition AT abdelsalamhamdi newimageenhancementandsuperresolutiontechniqueforlicenseplaterecognition AT yeekitchan newimageenhancementandsuperresolutiontechniqueforlicenseplaterecognition AT voonchetkoo newimageenhancementandsuperresolutiontechniqueforlicenseplaterecognition
_version_	1718400715612749824

A New Image Enhancement and Super Resolution technique for license plate recognition

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