Subband-adaptive image denoising based on contourlet transform

The contourlet transform is a new extension of the wavelet transform in two dimension.Because of its multiscale and directional properties,the contourlet transform can effectively capture the smooth contours that are the dominant features in natural images with only a small number of coefficients.We proposed a novel contourlet domain denoising method for subband-adaptive image denoising.The core of our approach is estimation of the probability that a given coefficient contains a significant noise-free component,which we call "signal of interest".In this respect we analyze case where the probability of signal presence is fixed per subband.All the probabilities are estimated assuming generalized Laplacian prior for noise-free subband data and additive white Gaussian noise.The experiment results show that the new subband-adaptive shrinkage denoising outperforms the Bayesian wavelet shrinkage and the contourlet HMT.