From local to global: a multi-group feature enhancement network for non-uniform and dense haze removal-Reference-Cited by-同舟云学术

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From local to global: a multi-group feature enhancement network for non-uniform and dense haze removal

Published:2023-03-02 Issue:17 Volume:82 Page:27057-27073
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Shao Xiaotao,Guo Yan,Shen Yan^ORCID,Qian Manyi,Wang Zhongli

Funder

the Joint Fund of Ministry of Education for Equipment Pre-research

National Science and Technology Innovation 2030 Major Program

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-14950-9.pdf

Reference45 articles.

1. Ancuti CO, Ancuti C, Sbert M et al (2019) Dense-haze: a benchmark for image dehazing with dense-haze and haze-free images [C]//2019 IEEE international conference on image processing (ICIP). IEEE 1014–1018

2. Ancuti CO, Ancuti C, Timofte R et al (2018) O-haze: a dehazing benchmark with real hazy and haze-free outdoor images [C]// Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 754–762

3. Berman D, Avidan S (2016) Non-local image dehazing [C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 1674–1682

4. Berman D, Treibitz T, Avidan S (2018) Single image dehazing using haze-lines [J]. IEEE Trans Pattern Anal Mach Intell 42(3):720–734

5. Bu Q, Luo J, Ma K, Feng H, Feng J (2020) An enhanced pix2pix dehazing network with guided filter layer [J]. Appl Sci 10(17):5898

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