Contrast Enhancement for Image Based on Wavelet Neural Network and Stationary Wavelet Transform

  • Changjiang Zhang
  • Xiaodong Wang
  • Haoran Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


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.


Simulated Annealing Algorithm Local Contrast Wavelet Neural Network Stationary Wavelet Transform Contrast Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Changjiang Zhang
    • 1
  • Xiaodong Wang
    • 1
  • Haoran Zhang
    • 1
  1. 1.College of Information Science and EngineeringZhejiang Normal UniversityJinhuaChina

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