IDGCP: Image Dehazing Based on Gamma Correction Prior

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

IDGCP: Image Dehazing Based on Gamma Correction Prior

Ju, M

Ding, C

Guo, YJ

Zhang, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2020, 29 pp. 3104 - 3118
Issue Date:: 2020-01-01

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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	Guo, YJ https://orcid.org/0000-0001-6008-9682	en_US
dc.contributor.author	Zhang, D https://orcid.org/0000-0001-6080-6151	en_US
dc.date.issued	2020-01-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2020, 29 pp. 3104 - 3118	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138131
dc.description.abstract	© 1992-2012 IEEE. This paper introduces a novel and effective image prior, i.e., gamma correction prior (GCP), which leads to an efficient image dehazing method, i.e., IDGCP. A step-by-step procedure of the proposed IDGCP is as follows. First, an input hazy image is preprocessed by the proposed GCP, resulting in a homogeneous virtual transformation of the hazy image. Then, from the original input hazy image and its virtual transformation, the depth ratio is extracted based on atmospheric scattering theory. Finally, a 'global-wise' strategy and a vision indicator are employed to recover the scene albedo, thus restoring the hazy image. Unlike other image dehazing methods, IDGCP is based on the 'global-wise' strategy, and it only needs to determine one unknown constant without any refining process to attain a high-quality restoration, thereby leading to significantly reduced processing time and computation cost. Each step of IDGCP is tested experimentally to validate its robustness. Moreover, a series of experiments are conducted on a number of challenging images with IDGCP and other state-of-the-art technologies, demonstrating the superiority of IDGCP over the others in terms of restoration quality and implementation efficiency.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2019.2957852	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	IDGCP: Image Dehazing Based on Gamma Correction Prior	en_US
dc.type	Journal Article
utslib.citation.volume	29	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	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	*
utslib.copyright.embargo	2021-12-19T00:00:00+1100
pubs.publication-status	Published	en_US
pubs.volume	29	en_US

Abstract:

© 1992-2012 IEEE. This paper introduces a novel and effective image prior, i.e., gamma correction prior (GCP), which leads to an efficient image dehazing method, i.e., IDGCP. A step-by-step procedure of the proposed IDGCP is as follows. First, an input hazy image is preprocessed by the proposed GCP, resulting in a homogeneous virtual transformation of the hazy image. Then, from the original input hazy image and its virtual transformation, the depth ratio is extracted based on atmospheric scattering theory. Finally, a 'global-wise' strategy and a vision indicator are employed to recover the scene albedo, thus restoring the hazy image. Unlike other image dehazing methods, IDGCP is based on the 'global-wise' strategy, and it only needs to determine one unknown constant without any refining process to attain a high-quality restoration, thereby leading to significantly reduced processing time and computation cost. Each step of IDGCP is tested experimentally to validate its robustness. Moreover, a series of experiments are conducted on a number of challenging images with IDGCP and other state-of-the-art technologies, demonstrating the superiority of IDGCP over the others in terms of restoration quality and implementation efficiency.

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http://hdl.handle.net/10453/138131