An Experiment-Based Review of Low-Light Image Enhancement Methods

直方图均衡化 人工智能 计算机科学 计算机视觉 亮度 颜色恒定性 直方图 图像增强 图像质量 模式识别(心理学) 图像(数学) 光学 物理
作者
Wencheng Wang,Xiaojin Wu,Xiaohui Yuan,Zairui Gao
出处
期刊:IEEE Access [Institute of Electrical and Electronics Engineers]
卷期号:8: 87884-87917 被引量:183
标识
DOI:10.1109/access.2020.2992749
摘要

Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested.
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