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Chin. Opt. Lett.
 Home  List of Issues    Issue 11 , Vol. 03 , 2005    Global and local contrast enhancement algorithm for image using wavelet neural network and stationary wavelet transform


Global and local contrast enhancement algorithm for image using wavelet neural network and stationary wavelet transform
Changjiang Zhang, Xiaodong Wang, Haoran Zhang
College of Information Science and Engineering, Zhejiang Normal University, Jinhua 321004

Chin. Opt. Lett., 2005, 03(11): pp.636-636-

DOI:
Topic:Image processing
Keywords(OCIS Code): 100.2980  100.2000  100.0100  100.7410  

Abstract
A new contrast enhancement algorithm for image is proposed employing wavelet neural network (WNN) and stationary wavelet transform (SWT). Incomplete Beta transform (IBT) is used to enhance the global contrast for image. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole gray transform parameter space, a new criterion is proposed with gray level histogram. Contrast type for original image is determined employing the new criterion. Gray transform parameter space is given respectively according to different contrast types, which shrinks the parameter space greatly. Nonlinear transform parameters are searched by simulated annealing algorithm (SA) so as to obtain optimal gray transform parameters. Thus the searching direction and selection of initial values of simulated annealing is guided by the new parameter space. In order to calculate IBT in the whole image, a kind of WNN is proposed to approximate the IBT. Having enhanced the global contrast to input image, discrete SWT is done to the image which has been processed by previous global enhancement method, local contrast enhancement is implemented by a kind of nonlinear operator in the high frequency sub-band images of each decomposition level respectively. Experimental results show that the new algorithm is able to adaptively enhance the global contrast for the original image while it also extrudes the detail of the targets in the original image well. The computation complexity for the new algorithm is O(MN)log(MN), where M and N are width and height of the original image, respectively.

Copyright: © 2003-2012 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Received:2005/4/25
Accepted:
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Get Citation: Changjiang Zhang, Xiaodong Wang, Haoran Zhang, "Global and local contrast enhancement algorithm for image using wavelet neural network and stationary wavelet transform," Chin. Opt. Lett. 03(11), 636-636-(2005)

Note: This work was supported in part by the Zhejiang Province Educational Office Foundation (No. 20050292) and the Zhejiang Normal University Foundation (No. 20041076). C. Zhang's e-mail address is zcj74922@zjnu.cn.



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