Locally-equalized image contrast enhancement using PSO-tuned sectorized equalization

Kwok, NM; Wang, D; Ha, QP; Fang, G; Chen, SY

Locally-equalized image contrast enhancement using PSO-tuned sectorized equalization

Kwok, NM Wang, D Ha, QP

Fang, G Chen, SY

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Publication Type:: Chapter
Citation:: Computational Intelligence in Image Processing, 2013, 9783642306211 pp. 21 - 36
Issue Date:: 2013-08-01

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Field	Value	Language
dc.contributor.author	Kwok, NM	en_US
dc.contributor.author	Wang, D	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Fang, G	en_US
dc.contributor.author	Chen, SY	en_US
dc.date.issued	2013-08-01	en_US
dc.identifier.citation	Computational Intelligence in Image Processing, 2013, 9783642306211 pp. 21 - 36	en_US
dc.identifier.isbn	3642306209	en_US
dc.identifier.isbn	9783642306204	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22201
dc.description.abstract	© 2013 Springer-Verlag Berlin Heidelberg. All rights are reserved. Contrast enhancement is a fundamental procedure in applications requiring image processing. Indeed, image enhancement contributes critically to the success of subsequent operations such as feature detection, pattern recognition and other higher-level processing tasks. Of interest among methods available for contrast enhancement is the intensity modification approach, which is based on the statistics of pixels in a given image. However, due to variations in the imaging condition and the nature of the scene being captured, it turns out that global manipulation of an image may be vulnerable to a noticeable quality degradation from distortion and noise. This chapter is devoted to the development of a local intensity equalization strategy together with mechanisms to remedy artifacts produced by the enhancement while ensuring a better image for viewing. To this end, the original image is subdivided randomly into sectors, which are equalized independently. A Gaussian weighting factor is further used to remove discontinuities along sector boundaries. To achieve simultaneously the multiple objectives of contrast enhancement and viewing distortion reduction, a suitable optimization algorithm is required to determine sector locations and the associated weighting factor. For this, a particle-swarm optimization algorithm is adopted in the proposed image enhancement method. This algorithm helps optimize the Gaussian weighting parameters for discontinuity removal and determine the local region where enhancement is applied. Following comprehensive descriptions on the methodology, this chapter presents some real-life images for illustration and verification of the effectiveness of the proposed approach.	en_US
dc.relation.ispartof	Computational Intelligence in Image Processing	en_US
dc.relation.isbasedon	10.1007/978-3-642-30621-1_2	en_US
dc.title	Locally-equalized image contrast enhancement using PSO-tuned sectorized equalization	en_US
dc.type	Chapter
utslib.citation.volume	9783642306211	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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 - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	9783642306211	en_US

Abstract:

© 2013 Springer-Verlag Berlin Heidelberg. All rights are reserved. Contrast enhancement is a fundamental procedure in applications requiring image processing. Indeed, image enhancement contributes critically to the success of subsequent operations such as feature detection, pattern recognition and other higher-level processing tasks. Of interest among methods available for contrast enhancement is the intensity modification approach, which is based on the statistics of pixels in a given image. However, due to variations in the imaging condition and the nature of the scene being captured, it turns out that global manipulation of an image may be vulnerable to a noticeable quality degradation from distortion and noise. This chapter is devoted to the development of a local intensity equalization strategy together with mechanisms to remedy artifacts produced by the enhancement while ensuring a better image for viewing. To this end, the original image is subdivided randomly into sectors, which are equalized independently. A Gaussian weighting factor is further used to remove discontinuities along sector boundaries. To achieve simultaneously the multiple objectives of contrast enhancement and viewing distortion reduction, a suitable optimization algorithm is required to determine sector locations and the associated weighting factor. For this, a particle-swarm optimization algorithm is adopted in the proposed image enhancement method. This algorithm helps optimize the Gaussian weighting parameters for discontinuity removal and determine the local region where enhancement is applied. Following comprehensive descriptions on the methodology, this chapter presents some real-life images for illustration and verification of the effectiveness of the proposed approach.

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http://hdl.handle.net/10453/22201