中文摘要：

提出一类新的非参数贝叶斯估计器，估计器利用正态反高斯（NIG）分布作为先验模型．与广义高斯分布（GGD）、alpha稳定分布和贝塞尔K分布（BKF）相比，正态反高斯分布能更加精确地对图像小波系数分布进行拟合．在二次型贝叶斯规则下，推导出基于正态反高斯分布的后验条件均值估计．最后，对图像进行去噪实验．实验结果表明，与最新提出的算法相比，该方法获得更高的峰值信噪比增益和好的视觉效果．

英文摘要：

A novel nonparametric Bayesian estimator for image denoising in wavelet domain is presented. In this approach, norreal inverse Gaussian （NIG） distribution is used as a prior model to capture the sparseness of the wavelet expansion. Compared with other distributions, such as the generalized Gaussian distribution （GGD）,α-stable models, and Bessel K forms （BKF）, it fits very well to the distributions of wavelet coefficients of natural images. Based on Lz based Bayes rules, a posterior conditional means estimator is designed. Finally, the estimator is used to image denoising. Experimental results show that compared with several recently published algorithms, the proposed method achieves state-of-art performance in terms of peak signal-to-noise ratio and visual effect.