A note on parametric image enhancement
Pattern Recognition
Digital Picture Processing
A novel fuzzy logic approach to mammogram contrast enhancement
Information Sciences—Applications: An International Journal
Contrast enhancement of color images based on Wavelet Transform and human visual system
GVE '07 Proceedings of the IASTED International Conference on Graphics and Visualization in Engineering
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Adaptive image contrast enhancement using generalizations of histogram equalization
IEEE Transactions on Image Processing
Joint Exact Histogram Specification and Image Enhancement Through the Wavelet Transform
IEEE Transactions on Image Processing
The Visual Computer: International Journal of Computer Graphics
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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.