Bivariate Statistical Modeling for Dual-tree Wavelet Coefficient in Image Denoising
基于双树复小波二元统计模型的图像去噪方法

In order to improve the denoising effect, a bivariate statistical modeling for dual-tree wavelet coefficient was proposed. This new denoising method used a parametric bivariate generalized Gaussian distribution (GGD)to describe the statistical distribution for Dual-tree complex wavelet coefficients of images. Then, based on maximum likelihood estimate (MLE), we can obtain the estimated parameters for GGD. With the estimated parameters, maximum a posteriori (MAP)estimator can be used to restore the wavelet coefficients from the noisy observations. Results of our experiments show that image noise can be reduced effectively while most image details can be kept. The proposed method outperforms many denoising algorithms both statistically and visually.