基于双树复小波二元统计模型的图像去噪方法

为了更有效地进行图像去噪，提出了一种基于双树复小波二元统计模型的图像去噪方法，该方法先用带参数的二元广义高斯分布(GGD)来模拟原图双树复小波系数的统计分布；然后结合最大似然估计(MLE)得到优化的参数估计；最后在此先验分布的基础上，运用最大后验概率(MAP)来估计从噪声图的小波系数中恢复原图的系数，从而达到去噪的目的。实验表明该新方法不仅可以干净地去除图像的噪声，还可以有效地保留图像细节，取得了良好的去噪效果，尤其是去噪图像的视觉效果要明显优于目前的很多算法。

Bivariate Statistical Modeling for Dual-tree Wavelet Coefficient in Image Denoising

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.