Enhancing Contrast for Image Using Discrete Stationary Wavelet Transform and Non-linear Gain Operator

Having implemented discrete stationary wavelet transform (DSWT) to an image, combining generalized cross validation (GCV), noise is reduced directly in the high frequency sub-bands which are at the better resolution levels and local contrast is enhanced by combining de-noising method with non-linear gain operator (NGO) in the high frequency sub-bands which are at the worse resolution levels. In order to enhance the global contrast for the image, the low frequency sub-band image is also enhanced employing in-complete Beta transform (IBT) and simulated annealing algorithm (SA). 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. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole gray transform parameters space, a new criterion is proposed with gray level histogram. Contrast type for original image is determined employing the new criterion. Gray transform parameters space is given respectively according to different contrast types, which shrinks gray transform parameters space greatly. Finally, 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 an image while reducing efficiently gauss white noise (GWN) in the image. The new algorithm is more excellent in performance than histogram equalization, un-sharpened mask algorithm, WYQ algorithm and GWP algorithm.

Authors and Affiliations

1. College of Information Science and Engineering, Zhejiang Normal University, Postcode 321004 Jinhua, China

   Changjiang Zhang, Xiaodong Wang & Haoran Zhang

Editors and Affiliations

1. Institute of Intelligent Machines , Chinese Academy of Sciences , Anhui, Hefei, China

   De-Shuang Huang

2. Queen’s University , Belfast, UK

   Kang Li  & George William Irwin  & 

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© 2006 Springer-Verlag Berlin Heidelberg

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