Gamma-correction-based visibility restoration for single hazy images

Ju, M; Ding, C; Zhang, D; Guo, YJ

Gamma-correction-based visibility restoration for single hazy images

Ju, M

Ding, C

Zhang, D Guo, YJ

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Publication Type:: Journal Article
Citation:: IEEE Signal Processing Letters, 2018, 25 (7), pp. 1084 - 1088
Issue Date:: 2018-07-01

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Field	Value	Language
dc.contributor.author	Ju, M https://orcid.org/0000-0003-4378-3781	en_US
dc.contributor.author	Ding, C https://orcid.org/0000-0002-2629-1657	en_US
dc.contributor.author	Zhang, D	en_US
dc.contributor.author	Guo, YJ https://orcid.org/0000-0001-6008-9682	en_US
dc.date.available	2020-07-02T19:10:36Z
dc.date.issued	2018-07-01	en_US
dc.identifier.citation	IEEE Signal Processing Letters, 2018, 25 (7), pp. 1084 - 1088	en_US
dc.identifier.issn	1070-9908	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128577
dc.description.abstract	© 1994-2012 IEEE. In this letter, a concise gamma-correction-based dehazing model (GDM) is proposed. This GDM explicitly describes the inner relationship between the gamma correction (GC) and the traditional scattering model. Combined with the existing priori constraints, GDM is further approximated into a one-dimensional (1-D) function to seek the only unknown constant that is used for haze removal. Using the determined constant, the scene albedo can be recovered, eliminating the haze from single hazy images. The proposed GDM is able to suppress the halo/blocking artifacts in the recovered results due to the scene albedo, which is less sensitive to the determined constant. Simulation results on different types of benchmark images verify that the proposed technique outperforms state-of-the-art methods in terms of both recovery, quality, and real-time performance.	en_US
dc.relation.ispartof	IEEE Signal Processing Letters	en_US
dc.relation.isbasedon	10.1109/LSP.2018.2839580	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Gamma-correction-based visibility restoration for single hazy images	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	25	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
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
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

© 1994-2012 IEEE. In this letter, a concise gamma-correction-based dehazing model (GDM) is proposed. This GDM explicitly describes the inner relationship between the gamma correction (GC) and the traditional scattering model. Combined with the existing priori constraints, GDM is further approximated into a one-dimensional (1-D) function to seek the only unknown constant that is used for haze removal. Using the determined constant, the scene albedo can be recovered, eliminating the haze from single hazy images. The proposed GDM is able to suppress the halo/blocking artifacts in the recovered results due to the scene albedo, which is less sensitive to the determined constant. Simulation results on different types of benchmark images verify that the proposed technique outperforms state-of-the-art methods in terms of both recovery, quality, and real-time performance.

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http://hdl.handle.net/10453/128577