Çelik, Turgay and Tjahjadi, Tardi. (2012) Automatic image equalization and contrast enhancement using Gaussian mixture modeling. IEEE Transactions on Image Processing, Vol.21 (No.1). pp. 145-156. ISSN 1057-7149

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Abstract

In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.

Item Type:	Journal Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH):	Image processing -- Digital techniques
Journal or Publication Title:	IEEE Transactions on Image Processing
Publisher:	IEEE
ISSN:	1057-7149
Date:	January 2012
Volume:	Vol.21
Number:	No.1
Number of Pages:	12
Page Range:	pp. 145-156
Identification Number:	10.1109/TIP.2011.2162419
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	University of Warwick
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URI:	http://wrap.warwick.ac.uk/id/eprint/40688

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