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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8667858/ https://www.ncbi.nlm.nih.gov/pubmed/34912381 http://dx.doi.org/10.3389/fgene.2021.799777 |
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author | Abirami R., Nandhini Vincent P. M., Durai Raj |
author_facet | Abirami R., Nandhini Vincent P. M., Durai Raj |
author_sort | Abirami R., Nandhini |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8667858 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86678582021-12-14 Low-Light Image Enhancement Based on Generative Adversarial Network Abirami R., Nandhini Vincent P. M., Durai Raj Front Genet Genetics 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. Frontiers Media S.A. 2021-11-29 /pmc/articles/PMC8667858/ /pubmed/34912381 http://dx.doi.org/10.3389/fgene.2021.799777 Text en Copyright © 2021 Abirami R. and Vincent P. M.. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Abirami R., Nandhini Vincent P. M., Durai Raj Low-Light Image Enhancement Based on Generative Adversarial Network |
title | 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_short | Low-Light Image Enhancement Based on Generative Adversarial Network |
title_sort | low-light image enhancement based on generative adversarial network |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8667858/ https://www.ncbi.nlm.nih.gov/pubmed/34912381 http://dx.doi.org/10.3389/fgene.2021.799777 |
work_keys_str_mv | AT abiramirnandhini lowlightimageenhancementbasedongenerativeadversarialnetwork AT vincentpmdurairaj lowlightimageenhancementbasedongenerativeadversarialnetwork |