An empirical study on Retinex methods for low-light image enhancement
Article
Rasheed, M., Guo, G., Shi, D., Khan, H. and Cheng, X. 2022. An empirical study on Retinex methods for low-light image enhancement. Remote Sensing. 14 (18). https://doi.org/10.3390/rs14184608
| Type | Article |
|---|---|
| Title | An empirical study on Retinex methods for low-light image enhancement |
| Authors | Rasheed, M., Guo, G., Shi, D., Khan, H. and Cheng, X. |
| 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. |
| Keywords | low-light image enhancement; retinex theory; deep learning; remote-sensing |
| Sustainable Development Goals | 9 Industry, innovation and infrastructure |
| Middlesex University Theme | Creativity, Culture & Enterprise |
| Publisher | MDPI |
| Journal | Remote Sensing |
| ISSN | |
| Electronic | 2072-4292 |
| Publication dates | |
| Online | 15 Sep 2022 |
| 02 Sep 2022 | |
| Publication process dates | |
| Submitted | 07 Aug 2022 |
| Accepted | 11 Sep 2022 |
| Deposited | 20 Aug 2024 |
| Output status | Published |
| Publisher's version | License File Access Level Open |
| Copyright Statement | Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
| Additional information | This article belongs to the Special Issue Advanced Machine Learning and Deep Learning Approaches for Remote Sensing |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/rs14184608 |
| Web of Science identifier | WOS:000859620900001 |
| Language | English |
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