Low-Light Image Enhancement Based on Generative Adversarial Network

Image enhancement is considered to be one of the complex tasks in image processing. When the images are captured under dim light, the quality of the images degrades due to low visibility degenerating the vision-based algorithms’ performance that is built for very good quality images with better visi...

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Autores principales:	Nandhini Abirami R., Durai Raj Vincent P. M.
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	computer vision deep learning facial expression recognition convolutional neural network human-robot interaction generative adversarial network Genetics QH426-470
Acceso en línea:	https://doaj.org/article/9f150441369549baa33e0179b06f4e30
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spelling	oai:doaj.org-article:9f150441369549baa33e0179b06f4e302021-12-01T14:21:45ZLow-Light Image Enhancement Based on Generative Adversarial Network1664-802110.3389/fgene.2021.799777https://doaj.org/article/9f150441369549baa33e0179b06f4e302021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.799777/fullhttps://doaj.org/toc/1664-8021Image enhancement is considered to be one of the complex tasks in image processing. When the images are captured under dim light, the quality of the images degrades due to low visibility degenerating the vision-based algorithms’ performance that is built for very good quality images with better visibility. After the emergence of a deep neural network number of methods has been put forward to improve images captured under low light. But, the results shown by existing low-light enhancement methods are not satisfactory because of the lack of effective network structures. A low-light image enhancement technique (LIMET) with a fine-tuned conditional generative adversarial network is presented in this paper. The proposed approach employs two discriminators to acquire a semantic meaning that imposes the obtained results to be realistic and natural. Finally, the proposed approach is evaluated with benchmark datasets. The experimental results highlight that the presented approach attains state-of-the-performance when compared to existing methods. The models’ performance is assessed using Visual Information Fidelitysse, which assesses the generated image’s quality over the degraded input. VIF obtained for different datasets using the proposed approach are 0.709123 for LIME dataset, 0.849982 for DICM dataset, 0.619342 for MEF dataset.Nandhini Abirami R.Durai Raj Vincent P. M.Frontiers Media S.A.articlecomputer visiondeep learningfacial expression recognitionconvolutional neural networkhuman-robot interactiongenerative adversarial networkGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	computer vision deep learning facial expression recognition convolutional neural network human-robot interaction generative adversarial network Genetics QH426-470
spellingShingle	computer vision deep learning facial expression recognition convolutional neural network human-robot interaction generative adversarial network Genetics QH426-470 Nandhini Abirami R. Durai Raj Vincent P. M. Low-Light Image Enhancement Based on Generative Adversarial Network
description	Image enhancement is considered to be one of the complex tasks in image processing. When the images are captured under dim light, the quality of the images degrades due to low visibility degenerating the vision-based algorithms’ performance that is built for very good quality images with better visibility. After the emergence of a deep neural network number of methods has been put forward to improve images captured under low light. But, the results shown by existing low-light enhancement methods are not satisfactory because of the lack of effective network structures. A low-light image enhancement technique (LIMET) with a fine-tuned conditional generative adversarial network is presented in this paper. The proposed approach employs two discriminators to acquire a semantic meaning that imposes the obtained results to be realistic and natural. Finally, the proposed approach is evaluated with benchmark datasets. The experimental results highlight that the presented approach attains state-of-the-performance when compared to existing methods. The models’ performance is assessed using Visual Information Fidelitysse, which assesses the generated image’s quality over the degraded input. VIF obtained for different datasets using the proposed approach are 0.709123 for LIME dataset, 0.849982 for DICM dataset, 0.619342 for MEF dataset.
format	article
author	Nandhini Abirami R. Durai Raj Vincent P. M.
author_facet	Nandhini Abirami R. Durai Raj Vincent P. M.
author_sort	Nandhini Abirami R.
title	Low-Light Image Enhancement Based on Generative Adversarial Network
title_short	Low-Light Image Enhancement Based on Generative Adversarial Network
title_full	Low-Light Image Enhancement Based on Generative Adversarial Network
title_fullStr	Low-Light Image Enhancement Based on Generative Adversarial Network
title_full_unstemmed	Low-Light Image Enhancement Based on Generative Adversarial Network
title_sort	low-light image enhancement based on generative adversarial network
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/9f150441369549baa33e0179b06f4e30
work_keys_str_mv	AT nandhiniabiramir lowlightimageenhancementbasedongenerativeadversarialnetwork AT durairajvincentpm lowlightimageenhancementbasedongenerativeadversarialnetwork
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Low-Light Image Enhancement Based on Generative Adversarial Network

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