标杆管理
计算机科学
水准点(测量)
人工智能
测距
任务(项目管理)
图像(数学)
计算机视觉
比例(比率)
物理
业务
电信
经济
营销
量子力学
管理
地理
大地测量学
作者
Boyi Li,Wenqi Ren,Dengpan Fu,Dacheng Tao,Dan Feng,Wenjun Zeng,Zhangyang Wang
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2018-08-30
卷期号:28 (1): 492-505
被引量:1392
标识
DOI:10.1109/tip.2018.2867951
摘要
In this paper, we present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of stateof- the-art dehazing algorithms, and suggest promising future directions.
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