工程科技文献索引服务平台 | abstract.cmanuf.com

Contrast enhancement for image by WNN and GA combining PSNR with information entropy

刊名：	Fuzzy Optimization and Decision Making
作者：	Chang-Jiang Zhang（College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China. e-mail: zcj74922@zjnu.cn） Min Hu
刊号：	519LB020
ISSN：	1568-4539
出版年：	2008
年卷期：	2008, vol.7, no.4
页码：	331-349
总页数：	19
分类号：	O22
关键词：	Contrast enhancement；Wavelet neural network；Genetic algorithm；In-complete Beta transform；Discrete stationary wavelet transform
参考中译：
语种：	eng
文摘：	A new contrast enhancement algorithm for image is proposed combining 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 enhancement 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 shrink parameters space. Thus searching direction of GA is guided by the new parameter space. Considering the drawback of traditional histogram equalization that it reduces the information and enlarges noise and background blur in the processed image, a synthetic objective function is used as fitness function of GA combining peak signal-noise-ratio (PSNR) and information 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, discrete stationary wavelet transform (DSWT) is used to enhance detail in an image. 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 blur from being greatly enlarged.