Abstract

A new contrast enhancement algorithm for image is proposed employing wavelet neural network (WNN) and stationary wavelet transform (SWT). Incomplete Beta transform (IBT) is used to enhance the global contrast for image. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole gray transform parameter space, a new criterion is proposed with gray level histogram. Contrast type for original image is determined employing the new criterion. Gray transform parameter space is given respectively according to different contrast types, which shrinks the parameter space greatly. Nonlinear transform parameters are searched by simulated annealing algorithm (SA) so as to obtain optimal gray transform parameters. Thus the searching direction and selection of initial values of simulated annealing is guided by the new parameter space. In order to calculate IBT in the whole image, a kind of WNN is proposed to approximate the IBT. Having enhanced the global contrast to input image, discrete SWT is done to the image which has been processed by previous global enhancement method, local contrast enhancement is implemented by a kind of nonlinear operator in the high frequency sub-band images of each decomposition level respectively. Experimental results show that the new algorithm is able to adaptively enhance the global contrast for the original image while it also extrudes the detail of the targets in the original image well. The computation complexity for the new algorithm is O(MN)log(MN), where M and N are width and height of the original image, respectively.

© 2005 Chinese Optics Letters

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2003 (2)

S.-M. Zhou and Q. Gan, ISPA2003 1, 11 (2003).

S. D. Chen and A. R. Ramli, IEEE Trans. Consumer Electron. 49, 1310 (2003).

2001 (1)

J. L. Zhou and H. Lu, Chinese J. Computers (in Chinese) 24, 959 (2001).

2000 (1)

M. Tang, S. D. Ma, and J. Xiao, Pattern Recognition Lett. 21, 827 (2000).

1997 (1)

J. D. Tubbs, Pattern Recognition 30, 616 (1997).

1995 (1)

M. Lang, H. Guo, J. E. Odegend, C. S. Burrus, and R. O. Wells, Proc. SPIE 2491, 640 (1995).

1994 (2)

A. Laine, S. Schuler, J. Fan, and W. Huda, IEEE Trans. Medical Imaging 14, 725 (1994).

W. L. Goffe, G. D. Ferrier, and J. Rogers, J. Econometrics 60, 65 (1994).

1992 (1)

Q. H. Zhang and A. Benveniste, IEEE Trans. NN 3, 889 (1992).

Chinese J. Computers (in Chinese) (1)

J. L. Zhou and H. Lu, Chinese J. Computers (in Chinese) 24, 959 (2001).

IEEE Trans. Consumer Electron. (1)

S. D. Chen and A. R. Ramli, IEEE Trans. Consumer Electron. 49, 1310 (2003).

IEEE Trans. Medical Imaging (1)

A. Laine, S. Schuler, J. Fan, and W. Huda, IEEE Trans. Medical Imaging 14, 725 (1994).

IEEE Trans. NN (1)

Q. H. Zhang and A. Benveniste, IEEE Trans. NN 3, 889 (1992).

ISPA2003 (1)

S.-M. Zhou and Q. Gan, ISPA2003 1, 11 (2003).

J. Econometrics (1)

W. L. Goffe, G. D. Ferrier, and J. Rogers, J. Econometrics 60, 65 (1994).

Pattern Recognition (1)

J. D. Tubbs, Pattern Recognition 30, 616 (1997).

Pattern Recognition Lett. (1)

M. Tang, S. D. Ma, and J. Xiao, Pattern Recognition Lett. 21, 827 (2000).

Proc. SPIE (1)

M. Lang, H. Guo, J. E. Odegend, C. S. Burrus, and R. O. Wells, Proc. SPIE 2491, 640 (1995).

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