AFF‐Dehazing: Attention‐based feature fusion network for low‐light image Dehazing-Reference-Cited by-同舟云学术

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AFF‐Dehazing: Attention‐based feature fusion network for low‐light image Dehazing

Published:2021-05-24 Issue:3-4 Volume:32 Page:
ISSN:1546-4261
Container-title:Computer Animation and Virtual Worlds
language:en
Short-container-title:Comput Anim Virtual Worlds

Author:

Zhou Yu¹,Chen Zhihua¹,Sheng Bin²^ORCID,Li Ping³^ORCID,Kim Jinman⁴,Wu Enhua⁵⁶

Affiliation:

1. Department of Computer Science and Engineering East China University of Science and Technology Shanghai China

2. Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

3. Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong

4. School of Information Technologies The University of Sydney Sydney Australia

5. State Key Laboratory of Computer Science Institute of Software, Chinese Academy of Sciences Beijing China

6. Faculty of Science and Technology University of Macau Macau China

Funder

Hong Kong Polytechnic University

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computer Graphics and Computer-Aided Design,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cav.2011

Reference23 articles.

1. ZhangJ CaoY FangS KangY ChenCW. Fast haze removal for nighttime image using maximum reflectance prior. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Honolulu HI;2017. p. 7016–24.

2. DongH PanJ XiangL HuZ ZhangX WangF et al. Multi‐scale boosted dehazing network with dense feature fusion. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Seattle WA;2020. p. 2154–64.

3. GuoC LiC GuoJ LoyCC HouJ KwongS et al. Zero‐reference deep curve estimation for low‐light image enhancement. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Seattle WA;2020. p. 1777–86.

4. QinX WangZ BaiY XieX JiaH. FFA‐Net: feature fusion attention network for single image dehazing. Proceedings of the AAAI Conference on Artificial Intelligence New York NY;2020. p. 11908–15.

5. Single Image Haze Removal Using Dark Channel Prior

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