Contrast Enhancement for Image Based on Wavelet Neural Network and Stationary Wavelet Transform
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.
KeywordsSimulated Annealing Algorithm Local Contrast Wavelet Neural Network Stationary Wavelet Transform Contrast Type
Unable to display preview. Download preview PDF.
- 1.Zhou, S.M., Qiang, G.: A New Fuzzy Relaxation Algorithm for Image Contrast Enhancement. In: Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis, Rome, Italy, vol. 1024, pp. 11–16 (2003)Google Scholar
- 4.Tubbs, J.D.: A Note on Parametric Image Enhancement. Pattern Recogn. 30, 616–621 (1997)Google Scholar
- 5.Li, H.G., Li, X.G., Li, G.Z., Luo, Z.F.: A Method for Infrared Image Enhancement Based On Genitic Algorithm. Chinese J. Systems Engineering and Electronics 21(5), 44–46 (1999)Google Scholar
- 6.Lang, M., Guo, H., Odegend, J.E.: Nonlinear Processing of a Shift-Invariant DWT For Noise Reduction. In: SPIE Conference on Wavelet Applications, vol. 2491, pp. 1211–1214 (1995)Google Scholar
- 8.Laine, A., Schuler, S.: Hexagonal Wavelet Processing Of Digital Mammography. In: Medical Imaging 1993, Part of SPIE’s Thematic Applied Science and Engineering Series, Newport Beach, California, vol. 1898, pp. 559–573 (1993)Google Scholar
- 9.Rosenfield, A., Avinash, C.: Digital Picture Processing. Academic Press, New York (1982)Google Scholar
- 10.Wang, M.L., Zhang, C.J., Fu, M.Y.: Simulation Study of a Kind of Wavelet Neural Network Algorithm Used in Approaching Non-Linear Functions. Journal of Beijing Institute of Technology 22(6), 274–278 (2002)Google Scholar