Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion

Tang, Q; Yang, J; He, X; Jia, W; Zhang, Q; Liu, H

Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion

Tang, Q Yang, J He, X

Jia, W

Zhang, Q Liu, H

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Computer Vision and Image Understanding, 2021, 202, pp. 103086-103086
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Tang, Q
dc.contributor.author	Yang, J
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338
dc.contributor.author	Zhang, Q
dc.contributor.author	Liu, H
dc.date.accessioned	2020-11-03T23:10:30Z
dc.date.available	2020-11-03T23:10:30Z
dc.date.issued	2021-01-01
dc.identifier.citation	Computer Vision and Image Understanding, 2021, 202, pp. 103086-103086
dc.identifier.issn	1077-3142
dc.identifier.issn	1090-235X
dc.identifier.uri	http://hdl.handle.net/10453/143738
dc.description.abstract	© 2020 Elsevier Inc. Haze removal from nighttime images is more difficult compared with daytime image dehazing due to the uneven illumination, low contrast and severe color distortion. In this paper, following the approaches based on Dark channel prior, we propose a simple yet effective approach using Retinex theory and Taylor series expansion for nighttime image dehazing, referred to as ‘RDT’. Existing nighttime image dehazing methods do not handle color shift and glow removal very well. In order to address these issues, we first propose to decompose the atmospheric light image from the input image based on the Retinex theory. Taylor series expansion is then introduced for the first time to accurately estimate the pointwise transmission map. Finally, during the following processes of image fusion and color transfer, the atmospheric light image and potential haze-free image are adopted to obtain the final haze-free image. The experimental results on benchmark nighttime haze images demonstrate the superior performance of our proposed RDT dehazing method over the state-of-the-art methods.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Computer Vision and Image Understanding
dc.relation.isbasedon	10.1016/j.cviu.2020.103086
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion
dc.type	Journal Article
utslib.citation.volume	202
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
utslib.copyright.embargo	2023-01-31T00:00:00+1000Z
dc.date.updated	2020-11-03T23:10:13Z
pubs.publication-status	Accepted
pubs.volume	202

Abstract:

© 2020 Elsevier Inc. Haze removal from nighttime images is more difficult compared with daytime image dehazing due to the uneven illumination, low contrast and severe color distortion. In this paper, following the approaches based on Dark channel prior, we propose a simple yet effective approach using Retinex theory and Taylor series expansion for nighttime image dehazing, referred to as ‘RDT’. Existing nighttime image dehazing methods do not handle color shift and glow removal very well. In order to address these issues, we first propose to decompose the atmospheric light image from the input image based on the Retinex theory. Taylor series expansion is then introduced for the first time to accurately estimate the pointwise transmission map. Finally, during the following processes of image fusion and color transfer, the atmospheric light image and potential haze-free image are adopted to obtain the final haze-free image. The experimental results on benchmark nighttime haze images demonstrate the superior performance of our proposed RDT dehazing method over the state-of-the-art methods.

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http://hdl.handle.net/10453/143738