Guo Jiawei, Ma Jieming, García-Fernández Ángel F, Zhang Yungang, Liang Haining
Department of Computer Science, University of Liverpool, Liverpool, UK.
School of Advanced Technology, Xi'an Jiaotong-Liverpool University (XJTLU), Suzhou, China.
Heliyon. 2023 Mar 16;9(4):e14558. doi: 10.1016/j.heliyon.2023.e14558. eCollection 2023 Apr.
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.
在真实场景中，由于光线不足和视角不合适等问题，图像常常呈现出各种退化现象，如对比度低、颜色失真和噪声。这些退化不仅影响视觉效果，还影响计算机视觉任务。本文重点关注图像增强领域中传统算法与机器学习算法的结合。从灰度变换、直方图均衡化和Retinex方法这三类介绍了传统方法，包括其原理和改进。基于机器学习的算法不仅分为端到端学习和无配对学习，还根据应用的图像处理策略归纳为基于分解的学习和基于融合的学习。最后，通过多种图像质量评估方法，包括均方误差、自然图像质量评估器、结构相似性、峰值信噪比等，对所涉及的方法进行了全面比较。
