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RW-HAZE: A REAL-WORLD BENCHMARK DATASET TO EVALUATE QUANTITATIVELY DEHAZING ALGORITHMS

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成果类型:
期刊论文、会议论文
作者:
Chen, Jiyou;Wang, Shengchun;Liu, Xin;Yang, Gaobo
通讯作者:
Chen, JY
作者机构:
[Chen, Jiyou; Yang, Gaobo] Hunan Univ, Sch Comp Sci & Elect Engn, Changsha 410082, Peoples R China.
[Chen, Jiyou] Hengyang Normal Univ, Sch Phys & Elect Engn, Hengyang 421008, Peoples R China.
[Wang, Shengchun] Hunan Normal Univ, Sch Informat Sci & Engn, Changsha 410081, Peoples R China.
[Liu, Xin] Hunan Appl Technol Univ, Changde 415100, Peoples R China.
通讯机构:
[Chen, JY ] H
Hunan Univ, Sch Comp Sci & Elect Engn, Changsha 410082, Peoples R China.
Hengyang Normal Univ, Sch Phys & Elect Engn, Hengyang 421008, Peoples R China.
语种:
英文
关键词:
image dehazing;benchmark dataset;performance evaluation
期刊:
International Conference on Image Processing. Proceedings
ISSN:
1522-4880
年:
2022
页码:
11-15
会议名称:
IEEE International Conference on Image Processing (ICIP)
会议论文集名称:
IEEE International Conference on Image Processing ICIP
会议时间:
OCT 16-19, 2022
会议地点:
Bordeaux, FRANCE
会议主办单位:
[Chen, Jiyou;Yang, Gaobo] Hunan Univ, Sch Comp Sci & Elect Engn, Changsha 410082, Peoples R China.^[Chen, Jiyou] Hengyang Normal Univ, Sch Phys & Elect Engn, Hengyang 421008, Peoples R China.^[Wang, Shengchun] Hunan Normal Univ, Sch Informat Sci & Engn, Changsha 410081, Peoples R China.^[Liu, Xin] Hunan Appl Technol Univ, Changde 415100, Peoples R China.
会议赞助商:
The Institute of Electrical and Electronics Engineers Signal Processing Society
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-1-6654-9620-9
基金类别:
National Natural Science Foundation of China [61972143, 61972142]; Scientific Research Fund of Hunan Provincial Education Department [21C0534]; Scientific Research Fund of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application [2021HSKFJJ040]
机构署名:
本校为通讯机构
摘要:
Most existing image dehazing approaches are able to achieve desirable results whose differences are too subtle for people to qualitatively judge. Therefore, it is important to adopt quantitative assessment on real-world hazy images. However, many dehazing works have not been quantitatively evaluated on real-world hazy images due to the lack of appropriate real-world datasets. In this work, we attempt to address the issue and present a well-aligned real-world benchmark dataset, namely RW-Haze, for image dehazing evaluation, which had been lackin...

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