计算机科学
人工智能
深度学习
背光
图像增强
计算机视觉
亮度
人工神经网络
对比度增强
图像(数学)
光学
物理
液晶显示器
操作系统
医学
磁共振成像
放射科
作者
Yong Wang,Wenjie Xie,Hongqi Liu
出处
期刊:Optical Engineering
[SPIE - International Society for Optical Engineering]
日期:2022-04-09
卷期号:61 (04)
被引量:17
标识
DOI:10.1117/1.oe.61.4.040901
摘要
Images taken under low light or dim backlight conditions usually have insufficient brightness, low contrast, and poor visual quality of the image, which leads to increased difficulty in computer vision and human recognition of images. Therefore, low illumination enhancement is very important in computer vision applications. We mainly provide an overview of existing deep learning enhancement algorithms in the low-light field. First, a brief overview of the traditional enhancement algorithms used in early low-light images is given. Then, according to the neural network structure used in deep learning and its learning algorithm, the enhancement methods are introduced. In addition, the datasets and common performance indicators used in the deep learning enhancement technology are introduced. Finally, the problems and future development of the deep learning enhancement method for low-light images are described.
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