Contrast enhancement for image based on discrete stationary wavelet transform

A new algorithm to enhance contrast for image is proposed, which based on discrete stationary wavelet transform (DSWT) and non-linear gain operator. Comparing with usual discrete orthogonal wavelet transform, DSWT is redundant and shift-invariant. It can give a more approximate estimation to continuous wavelet transform. It can eliminate the Gibbs phenomena when image is reconstructed. This will improve greatly quality of reconstructed image. Combining DSWT with generalized cross validation principle, a new de-noising algorithm to image is proposed. The new de-noising algorithm can restrain efficiently white noise and colored noise in the image without prior-knowing variance of noise in the image. An asymptotical optical threshold can be obtained by only data of original image. Having made DSWT to an image, de-noising is done with proposed algorithm in the high frequency sub-bands in the better resolution levels. Contrast is enhanced by combining de-noising algorithm with non-linear gain operator in the high frequency sub-bands in the worse resolution levels. A new criterion to evaluate quality of enhanced image is given. Experimental results show that the new algorithm can suppress white noise and colored noise in the image effectively while it also enhances the contrast of image well. The proposed new algorithm is more excellent in performance than histogram equalization, un-sharpened mask algorithm, WYQ algorithm and GWP algorithm.