Non-linear Algorithm for Contrast Enhancement for Image Using Wavelet Neural Network
Jianmao Xu, Sun Junzhong, Changjiang Zhang · 2006
A kind of contrast enhancement algorithm for image is proposed by employing in-complete Beta transform (IBT) and wavelet neural network (WNN). IBT is used to enhance the contrast of an image. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole gray transform parameters space, a new criterion is proposed with gray level histogram. Contrast type of original image is determined by the new criterion. Gray transform parameters space is respectively determined by different contrast types, which shrinks gray transform parameters space greatly. Nonlinear transform parameters are searched by simulated annealing algorithm (SA) so as to obtain optimal gray transform parameters. 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 global contrast for the original image well. The computation for the new algorithm is O (MN), where M and N are width and height of the original image