An Empirical Study on Retinex Methods for Low-Light Image Enhancement

Rasheed, Muhammad Tahir; Guo, Guiyu; Shi, Daming; Khan, Hufsa; Cheng, Cheng

doi:10.3390/rs14184608

Journal article 232 views 34 downloads

An Empirical Study on Retinex Methods for Low-Light Image Enhancement

Muhammad Tahir Rasheed

, Guiyu Guo, Daming Shi, Hufsa Khan

, Cheng Cheng

Remote Sensing, Volume: 14, Issue: 18, Start page: 4608

Swansea University Author: Cheng Cheng

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DOI (Published version): 10.3390/rs14184608

Abstract

A key part of interpreting, visualizing, and monitoring the surface conditions of remote-sensing images is enhancing the quality of low-light images. It aims to produce higher contrast, noise-suppressed, and better quality images from the low-light version. Recently, Retinex theory-based enhancement...

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Published in:	Remote Sensing
ISSN:	2072-4292
Published:	MDPI AG 2022
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa65955
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spelling	v2 65955 2024-04-03 An Empirical Study on Retinex Methods for Low-Light Image Enhancement 11ddf61c123b99e59b00fa1479367582 0000-0003-0371-9646 Cheng Cheng Cheng Cheng true false 2024-04-03 MACS A key part of interpreting, visualizing, and monitoring the surface conditions of remote-sensing images is enhancing the quality of low-light images. It aims to produce higher contrast, noise-suppressed, and better quality images from the low-light version. Recently, Retinex theory-based enhancement methods have gained a lot of attention because of their robustness. In this study, Retinex-based low-light enhancement methods are compared to other state-of-the-art low-light enhancement methods to determine their generalization ability and computational costs. Different commonly used test datasets covering different content and lighting conditions are used to compare the robustness of Retinex-based methods and other low-light enhancement techniques. Different evaluation metrics are used to compare the results, and an average ranking system is suggested to rank the enhancement methods. Journal Article Remote Sensing 14 18 4608 MDPI AG 2072-4292 low-light image enhancement; retinex theory; deep learning; remote-sensing 15 9 2022 2022-09-15 10.3390/rs14184608 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee This work is supported by Ministry of Science and Technology China (MOST) Major Program on New Generation of Artificial Intelligence 2030 No. 2018AAA0102200. It is also supported by Natural Science Foundation China (NSFC) Major Project No. 61827814 and Shenzhen Science and Technology Innovation Commission (SZSTI) project No. JCYJ20190808153619413. 2024-05-29T14:40:03.6822573 2024-04-03T17:50:35.2326123 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Muhammad Tahir Rasheed 0000-0001-5898-4688 1 Guiyu Guo 2 Daming Shi 3 Hufsa Khan 0000-0002-0037-1448 4 Cheng Cheng 0000-0003-0371-9646 5 65955__30479__4255918a0543473eb141465db39adae9.pdf 65955.VoR.pdf 2024-05-29T14:38:58.4621424 Output 36596324 application/pdf Version of Record true © 2022 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title	An Empirical Study on Retinex Methods for Low-Light Image Enhancement
spellingShingle	An Empirical Study on Retinex Methods for Low-Light Image Enhancement Cheng Cheng
title_short	An Empirical Study on Retinex Methods for Low-Light Image Enhancement
title_full	An Empirical Study on Retinex Methods for Low-Light Image Enhancement
title_fullStr	An Empirical Study on Retinex Methods for Low-Light Image Enhancement
title_full_unstemmed	An Empirical Study on Retinex Methods for Low-Light Image Enhancement
title_sort	An Empirical Study on Retinex Methods for Low-Light Image Enhancement
author_id_str_mv	11ddf61c123b99e59b00fa1479367582
author_id_fullname_str_mv	11ddf61c123b99e59b00fa1479367582_***_Cheng Cheng
author	Cheng Cheng
author2	Muhammad Tahir Rasheed Guiyu Guo Daming Shi Hufsa Khan Cheng Cheng
format	Journal article
container_title	Remote Sensing
container_volume	14
container_issue	18
container_start_page	4608
publishDate	2022
institution	Swansea University
issn	2072-4292
doi_str_mv	10.3390/rs14184608
publisher	MDPI AG
college_str	Faculty of Science and Engineering
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hierarchy_top_title	Faculty of Science and Engineering
hierarchy_parent_id	facultyofscienceandengineering
hierarchy_parent_title	Faculty of Science and Engineering
department_str	School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description	A key part of interpreting, visualizing, and monitoring the surface conditions of remote-sensing images is enhancing the quality of low-light images. It aims to produce higher contrast, noise-suppressed, and better quality images from the low-light version. Recently, Retinex theory-based enhancement methods have gained a lot of attention because of their robustness. In this study, Retinex-based low-light enhancement methods are compared to other state-of-the-art low-light enhancement methods to determine their generalization ability and computational costs. Different commonly used test datasets covering different content and lighting conditions are used to compare the robustness of Retinex-based methods and other low-light enhancement techniques. Different evaluation metrics are used to compare the results, and an average ranking system is suggested to rank the enhancement methods.
published_date	2022-09-15T14:40:02Z
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An Empirical Study on Retinex Methods for Low-Light Image Enhancement

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