Single Image Dehazing Method Based on Gradient Matrix and Ternary Parameter Search

Chen, C; Liu, J; Chen, K; Ju, M

Single Image Dehazing Method Based on Gradient Matrix and Ternary Parameter Search

Chen, C Liu, J Chen, K Ju, M

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP), 2021, 00, pp. 1-5
Issue Date:: 2021-12-01

Closed Access

	Filename	Description	Size
	Single_Image_Dehazing_Method_Based_on_Gradient_Matrix_and_Ternary_Parameter_Search.pdf	Published version	6.68 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Chen, C
dc.contributor.author	Liu, J
dc.contributor.author	Chen, K
dc.contributor.author	Ju, M https://orcid.org/0000-0003-4378-3781
dc.date	2021-10-20
dc.date.accessioned	2022-06-11T06:17:37Z
dc.date.available	2022-06-11T06:17:37Z
dc.date.issued	2021-12-01
dc.identifier.citation	2021 13th International Conference on Wireless Communications and Signal Processing (WCSP), 2021, 00, pp. 1-5
dc.identifier.isbn	978-1-6654-0785-4
dc.identifier.issn	2325-3746
dc.identifier.issn	2472-7628
dc.identifier.uri	http://hdl.handle.net/10453/158102
dc.description.abstract	Hazy images typically suffer from low contrast and poor visibility, which influences the performance of many computer vision systems. In this paper, a single image dehazing method called DG T is proposed. First of all, by employing the dark channel prior, atmospheric light and rough transmission map can be obtained. In subsequence, via utilizing the gradient matrix, the object details in the images can be enhanced. Finally, built on a minimum information loss principle, a ternary search algorithm is employed to determine the related parameters, and thus effectively restore the hazy images. Different from the previous works, DGT only needs to determine one unknown parameter with the cost of logarithmic time complexity. Experimental results show the superiority of the proposed method over state-of-the-art dehazing methods.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 13th International Conference on Wireless Communications and Signal Processing (WCSP)
dc.relation.ispartof	International Conference on Wireless Communications and Signal Processing
dc.relation.isbasedon	10.1109/wcsp52459.2021.9613598
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Single Image Dehazing Method Based on Gradient Matrix and Ternary Parameter Search
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Changsha, China
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	*
pubs.consider-herdc	false
dc.date.updated	2022-06-11T06:17:33Z
pubs.finish-date	2021-10-22
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-10-20
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Hazy images typically suffer from low contrast and poor visibility, which influences the performance of many computer vision systems. In this paper, a single image dehazing method called DG T is proposed. First of all, by employing the dark channel prior, atmospheric light and rough transmission map can be obtained. In subsequence, via utilizing the gradient matrix, the object details in the images can be enhanced. Finally, built on a minimum information loss principle, a ternary search algorithm is employed to determine the related parameters, and thus effectively restore the hazy images. Different from the previous works, DGT only needs to determine one unknown parameter with the cost of logarithmic time complexity. Experimental results show the superiority of the proposed method over state-of-the-art dehazing methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/158102