Contrast Enhancement for Image by WNN and GA Combining PSNR with Information Entropy

Abstract

A new contrast enhancement algorithm for image is proposed combing genetic algorithm (GA) with wavelet neural network (WNN). In-complete Beta transform (IBT) is used to obtain non-linear gray transform curve so as to enhance global contrast for an image. GA determines optimal gray transform parameters. In order to avoid the expensive time for traditional contrast en-hancement algorithms, which search optimal gray transform parameters in the whole parameters space, based on gray distribution of an image, a classification criterion is proposed. Contrast type for original image is determined by the new criterion. Parameters space is respectively determined according to different contrast types, which greatly shrinks parameters space. Thus searching direction of GA is guided by the new parameter space. Considering the drawback of trad-tional histogram equalization that it reduces the information and enlarges noise and background blutter in the processed image, a synthetic objective function is used as fitnees function of GA combing peak signal-noise-ratio (PSNR) and in-formation entropy. In order to calculate IBT in the whole image, WNN is used to approximate the IBT. In order to enhance the local contrast for image, dis-crete stationary wavelet transform (DSWT) is used to enhance detail in an im-age. Having implemented DSWT to an image, detail is enhanced by a non-linear operator in three high frequency sub-bands. The coefficients in the low frequency sub-bands are set as zero. Final enhanced image is obtained by adding the global enhanced image with the local enhanced image. Experimental results show that the new algorithm is able to well enhance the global and local contrast for image while keeping the noise and background blutter from being greatly enlarged.