水准点(测量)
计算机科学
任务(项目管理)
图像(数学)
测距
人工智能
多样性(控制论)
比例(比率)
计算机视觉
评价方法
机器学习
电信
地图学
地理
管理
工程类
可靠性工程
经济
作者
Boyi Li,Wenqi Ren,Dengpan Fu,Dacheng Tao,Dan Feng,Wenjun Zeng,Zhangyang Wang
出处
期刊:Cornell University - arXiv
日期:2017-12-12
被引量:67
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 sheds light on the comparisons and limitations of state-of-the-art dehazing algorithms, and suggest promising future directions.
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