Contrast enhancement for image based on wavelet neural network and stationary wavelet transform

Authors:
Changjiang Zhang;Xiaodong Wang;Haoran Zhang
Affiliations:
College of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;College of Information Science and Engineering, Zhejiang Normal University, Jinhua, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

After performing discrete stationary wavelet transform (DSWT) to an image, local contrast is enhanced with non-linear operator in the high frequency sub-bands, which are at coarser resolution levels. In order to enhance global contrast for an infrared image, low frequency sub-band image is also enhanced employing non-incomplete Beta transform (IBT), simulated annealing algorithm (SA) and wavelet neural network (WNN). IBT is used to obtain non-linear gray transform curve. Transform parameters are determined by SA so as to obtain optimal non-linear gray transform parameters. Contrast type of original image is determined by a new criterion. Gray transform parameters space is determined respectively according to different contrast types. A kind of WNN is proposed to approximate the IBT in the whole low frequency sub-band image. The quality of enhanced image is evaluated by a total cost criterion. Experimental results show that the new algorithm can improve greatly the global and local contrast for images.