Contrast enhancement for image with non-linear gray transform and wavelet neural network

A new contrast enhancement algorithm for image is proposed with non-linear gray transform and wavelet neural network (WNN). In-complete Beta transform (IBT) is used to obtain non-linear gray transform curve. Transform parameters are determined by simulated annealing algorithm (SA) to obtain optimal s space, a new criterion is proposed. Contrast type for original image is determined employing the new criterion. Parameters space is given respectively according to different contrast types, which shrinks parameters space greatly. Thus searching direction and selegray transform parameters. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole parameterction of initial values of SA 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. Experimental results show that the new algorithm is able to adaptively enhance the contrast for image well.