计算机科学
薄雾
观点
水准点(测量)
图像(数学)
领域(数学)
人工智能
计算机视觉
主流
图像质量
数学
地质学
艺术
气象学
视觉艺术
哲学
物理
纯数学
神学
大地测量学
作者
Fan Guo,Jianan Yang,Zhuoqun Liu,Jin Tang
出处
期刊:Neurocomputing
[Elsevier BV]
日期:2023-03-31
卷期号:537: 85-109
被引量:25
标识
DOI:10.1016/j.neucom.2023.03.061
摘要
Image dehazing is always a hot topic in the field of computer vision since haze has significant impact on the imaging quality of camera. Therefore, many image dehazing methods have been proposed for the past decades. To help researchers who are new to this field quickly figure out the development history as well as the current status of image dehazing, this review analyzes some representative dehazing methods, evaluates their advantages and disadvantages, and most importantly, points out the best dehazing method from different viewpoints. A large quantity of experiments show that AECR-Net can be generally considered to be the best dehazing algorithm and Tarel method can be regarded as the best real-time dehazing method. Besides, the mainstream benchmark, metrics, challenges and opportunities for image dehazing are also discussed in this review.
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