Guo, Jiawei, Ma, Jieming, García-Fernández, Ángel F, Zhang, Yungang and Liang, Haining
(2023)
A survey on image enhancement for Low-light images.
Heliyon, 9 (4).
e14558-e14558.
Abstract
In real scenes, due to the problems of low light and unsuitable views, the images often exhibit a variety of degradations, such as low contrast, color distortion, and noise. These degradations affect not only visual effects but also computer vision tasks. This paper focuses on the combination of traditional algorithms and machine learning algorithms in the field of image enhancement. The traditional methods, including their principles and improvements, are introduced from three categories: gray level transformation, histogram equalization, and Retinex methods. Machine learning based algorithms are not only divided into end-to-end learning and unpaired learning, but also concluded to decomposition-based learning and fusion based learning based on the applied image processing strategies. Finally, the involved methods are comprehensively compared by multiple image quality assessment methods, including mean square error, natural image quality evaluator, structural similarity, peak signal to noise ratio, etc.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Image enhancement, Low-light images, Image processing, Deep learning |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 31 Mar 2023 09:57 |
| Last Modified: | 28 Jun 2023 18:50 |
| DOI: | 10.1016/j.heliyon.2023.e14558 |
| Open Access URL: | https://reader.elsevier.com/reader/sd/pii/S2405844... |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3169380 |
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