Enhancing global and local contrast for image using discrete stationary wavelet transform and simulated annealing algorithm

  • Authors:

  • Changjiang Zhang;C. J. Duanmu;Xiaodong Wang

  • Affiliations:

  • Dept. of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;Dept. of Information Science and Engineering, Zhejiang Normal University, Jinhua, China;Dept. of Information Science and Engineering, Zhejiang Normal University, Jinhua, China

  • Venue:
  • KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
  • Year:
  • 2006

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Abstract

After the discrete stationary wavelet transform (DSWT) combined with the generalized cross validation (GCV) for an image, the noise in the image is directly reduced in the high frequency sub-bands, which are at the high- resolution levels. Then the local contrast of the image is enhanced by combining de-noising method with in-complete Beta transform (IBT) in the high frequency sub-bands, which are at the low-resolution levels. In order to enhance the global contrast for the image, the low frequency sub-band image is also processed by combining the IBT and the simulated annealing algorithm (SA). The IBT is used to obtain the non-linear gray transform curve. The transform parameters are determined by the SA so as to obtain the optimal non-linear gray transform parameters. In order to reduce the large computational requirements of traditional contrast enhancement algorithms, a new criterion is proposed with the gray level histogram. The contrast type for an original image is determined by employing the new criterion. The gray transform parameters space is respectively given according to different contrast types, which greatly shrinks gray transform parameters space. Finally, the quality of the enhanced image is evaluated by a new overall objective criterion. Experimental results demonstrate that the new algorithm can greatly improve the global and local contrast for an image while efficiently reducing gauss white noise (GWN) in the image. The new algorithm performs better than the histogram equalization (HE) algorithm, un-sharpened mask algorithm (USM), Tubbs’s algorithm [2], Gong’s algorithm [3] and Wu’s algorithm [4].