A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms

Shen, M; Lv, T; Liu, Y; Zhang, J; Ju, M

A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms

Shen, M Lv, T Liu, Y Zhang, J Ju, M

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: ELECTRONICS, 2024, 13, (17)
Issue Date:: 2024-09

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Field	Value	Language
dc.contributor.author	Shen, M
dc.contributor.author	Lv, T
dc.contributor.author	Liu, Y
dc.contributor.author	Zhang, J
dc.contributor.author	Ju, M https://orcid.org/0000-0003-4378-3781
dc.date.accessioned	2024-10-10T00:13:47Z
dc.date.available	2024-10-10T00:13:47Z
dc.date.issued	2024-09
dc.identifier.citation	ELECTRONICS, 2024, 13, (17)
dc.identifier.issn	1450-5843
dc.identifier.issn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10453/181357
dc.description.abstract	<jats:p>Images captured under adverse weather conditions often suffer from blurred textures and muted colors, which can impair the extraction of reliable information. Image defogging has emerged as a critical solution in computer vision to enhance the visual quality of such foggy images. However, there remains a lack of comprehensive studies that consolidate both traditional algorithm-based and deep learning-based defogging techniques. This paper presents a comprehensive survey of the currently proposed defogging techniques. Specifically, we first provide a fundamental classification of defogging methods: traditional techniques (including image enhancement approaches and physical-model-based defogging) and deep learning algorithms (such as network-based models and training strategy-based models). We then delve into a detailed discussion of each classification, introducing several representative image fog removal methods. Finally, we summarize their underlying principles, advantages, disadvantages, and give the prospects for future development.</jats:p>
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	ELECTRONICS
dc.relation.isbasedon	10.3390/electronics13173392
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-10-10T00:13:45Z
pubs.issue	17
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	17

Abstract:

Images captured under adverse weather conditions often suffer from blurred textures and muted colors, which can impair the extraction of reliable information. Image defogging has emerged as a critical solution in computer vision to enhance the visual quality of such foggy images. However, there remains a lack of comprehensive studies that consolidate both traditional algorithm-based and deep learning-based defogging techniques. This paper presents a comprehensive survey of the currently proposed defogging techniques. Specifically, we first provide a fundamental classification of defogging methods: traditional techniques (including image enhancement approaches and physical-model-based defogging) and deep learning algorithms (such as network-based models and training strategy-based models). We then delve into a detailed discussion of each classification, introducing several representative image fog removal methods. Finally, we summarize their underlying principles, advantages, disadvantages, and give the prospects for future development.

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http://hdl.handle.net/10453/181357