VRHI: Visibility Restoration for Hazy Images Using a Haze Density Model

Ju, M; Chen, C; Liu, J; Cheng, K; Zhang, D

VRHI: Visibility Restoration for Hazy Images Using a Haze Density Model

Ju, M

Chen, C Liu, J Cheng, K Zhang, D

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021, 00, pp. 897-904
Issue Date:: 2021-09-01

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Field	Value	Language
dc.contributor.author	Ju, M https://orcid.org/0000-0003-4378-3781
dc.contributor.author	Chen, C
dc.contributor.author	Liu, J
dc.contributor.author	Cheng, K
dc.contributor.author	Zhang, D
dc.date	2021-06-19
dc.date.accessioned	2022-06-04T22:42:09Z
dc.date.available	2022-06-04T22:42:09Z
dc.date.issued	2021-09-01
dc.identifier.citation	2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021, 00, pp. 897-904
dc.identifier.isbn	9781665448994
dc.identifier.issn	2160-7508
dc.identifier.issn	2160-7516
dc.identifier.uri	http://hdl.handle.net/10453/157927
dc.description.abstract	In this paper, a image processing method called VRHI is developed to enhance single hazy images. More specifically, inspired by visual characteristics of haze, a haze density estimation model is designed to predict the haze distribution. According to this recognized haze distribution, a quadtree based recursive strategy is subsequently proposed to locate the atmospheric light. Finally, by combining a global-wise adjusting mechanism and atmospheric scattering model, the haze cover in an image can be easily excluded using the estimated parameters. It is worth mentioning that VRHI is based on whole image to search the unknown parameters, thereby avoiding some unfavorable phenomena, e.g., over-enhancement and color distortion. Extensive experiments on real-world images and well-known dehazing datasets show that VRHI outperforms state-of-the-art techniques in robustness and effectiveness.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
dc.relation.ispartof	2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
dc.relation.isbasedon	10.1109/cvprw53098.2021.00100
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	VRHI: Visibility Restoration for Hazy Images Using a Haze Density Model
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Nashville, TN, USA
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	closed_access	*
dc.date.updated	2022-06-04T22:42:01Z
pubs.finish-date	2021-06-25
pubs.publication-status	Published
pubs.start-date	2021-06-19
pubs.volume	00

Abstract:

In this paper, a image processing method called VRHI is developed to enhance single hazy images. More specifically, inspired by visual characteristics of haze, a haze density estimation model is designed to predict the haze distribution. According to this recognized haze distribution, a quadtree based recursive strategy is subsequently proposed to locate the atmospheric light. Finally, by combining a global-wise adjusting mechanism and atmospheric scattering model, the haze cover in an image can be easily excluded using the estimated parameters. It is worth mentioning that VRHI is based on whole image to search the unknown parameters, thereby avoiding some unfavorable phenomena, e.g., over-enhancement and color distortion. Extensive experiments on real-world images and well-known dehazing datasets show that VRHI outperforms state-of-the-art techniques in robustness and effectiveness.

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http://hdl.handle.net/10453/157927