Haze removal for single image: A comprehensive review

被引:13
|
作者
Guo, Fan [1 ]
Yang, Jianan [1 ]
Liu, Zhuoqun [1 ]
Tang, Jin [1 ]
机构
[1] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China
关键词
Image dehazing; Enhancement; Restoration; Fusion; Deep learning; CONTRAST ENHANCEMENT; QUALITY ASSESSMENT; HISTOGRAM EQUALIZATION; VISIBILITY; VISION; NETWORK; WEATHER; RESTORATION; FRAMEWORK;
D O I
10.1016/j.neucom.2023.03.061
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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. (c) 2023 Elsevier B.V. All rights reserved.
引用
收藏
页码:85 / 109
页数:25
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