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An Empirical Study on Retinex Methods for Low-Light Image Enhancement
,
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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© 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.
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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...
| Published in: | Remote Sensing |
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| ISSN: | 2072-4292 |
| Published: |
MDPI AG
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65955 |
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| 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 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. |
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| Keywords: |
low-light image enhancement; retinex theory; deep learning; remote-sensing |
| College: |
Faculty of Science and Engineering |
| Funders: |
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. |
| Issue: |
18 |
| Start Page: |
4608 |