Intensity-preserving contrast enhancement for gray-level images using multi-objective particle swarm optimization

Kwok, NM; Ha, QP; Liu, DK; Fang, G

Intensity-preserving contrast enhancement for gray-level images using multi-objective particle swarm optimization

Kwok, NM Ha, QP

Liu, DK

Fang, G

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Publication Type:: Conference Proceeding
Citation:: 2006 IEEE International Conference on Automation Science and Engineering, CASE, 2006, pp. 21 - 26
Issue Date:: 2006-01-01

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Field	Value	Language
dc.contributor.author	Kwok, NM	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Liu, DK https://orcid.org/0000-0002-1581-5582	en_US
dc.contributor.author	Fang, G	en_US
dc.date.issued	2006-01-01	en_US
dc.identifier.citation	2006 IEEE International Conference on Automation Science and Engineering, CASE, 2006, pp. 21 - 26	en_US
dc.identifier.isbn	1424403103	en_US
dc.identifier.isbn	9781424403103	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2852
dc.description.abstract	This paper addresses the enhancement of the contrast of gray-level digital images while preserving the mean image intensity, thus, providing better viewing consistence and effectiveness. The contrast enhancement is achieved by maximizing the information content carried in the image with a continuous intensity transform function and the mean image intensity is preserved, by using the gamma-correction approach. Since the contrast enhancement and intensity preservation are contradicting, a multi-objective particle swarm optimization (MPSO) algorithm is developed to resolve this trade-off. Benchmark images, street senses and skyline images are included to illustrate the effectiveness of the proposed approach. © 2006 IEEE.	en_US
dc.relation.ispartof	2006 IEEE International Conference on Automation Science and Engineering, CASE	en_US
dc.relation.isbasedon	10.1109/COASE.2006.326849	en_US
dc.title	Intensity-preserving contrast enhancement for gray-level images using multi-objective particle swarm optimization	en_US
dc.type	Conference Proceeding
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0910 Manufacturing Engineering	en_US
dc.location.activity	Shanghai, China	en_US
dc.location.activity	Shanghai, PEOPLES R CHINA
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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper addresses the enhancement of the contrast of gray-level digital images while preserving the mean image intensity, thus, providing better viewing consistence and effectiveness. The contrast enhancement is achieved by maximizing the information content carried in the image with a continuous intensity transform function and the mean image intensity is preserved, by using the gamma-correction approach. Since the contrast enhancement and intensity preservation are contradicting, a multi-objective particle swarm optimization (MPSO) algorithm is developed to resolve this trade-off. Benchmark images, street senses and skyline images are included to illustrate the effectiveness of the proposed approach. © 2006 IEEE.

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http://hdl.handle.net/10453/2852