能见度
人工智能
计算机科学
计算机视觉
失真(音乐)
亮度
水准点(测量)
感知
工厂(面向对象编程)
亮度
模式识别(心理学)
地图学
光学
地理
物理
放大器
计算机网络
带宽(计算)
神经科学
生物
程序设计语言
作者
Miaohui Wang,Yijing Huang,Jian Xiong,Weixin Xie
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:19 (4): 6026-6036
被引量:2
标识
DOI:10.1109/tii.2022.3173934
摘要
Owing to the increasing deployment of CMOS camera modules, it is inevitable to take photographs under weak illumination. Therefore, low-light imaging quality is one of the most important factors affecting user experience as well as the product values of consumer electronics, automobile, surveillance, factory automation, and other industrial applications. Inspired by human vision, this article jointly considers visibility perception , luminosity cognition , and color sensation and presents a new visibility perception-guided blind quality indicator for low-light images in-the-wild. To excavate effective descriptors for authentic distortions under weak illumination, we utilize maximum ignorable visible difference to characterize the reduced visibility, and employ the luminance statistical properties and color sensation characteristics to represent brightness and colorfulness distortions. Extensive experimental results on the benchmark dataset verify that the proposed blind quality indicator outperforms nine representative methods including general-purpose and distortion-specific methods.
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