中文摘要：

研究了curvelet变换域非参数贝叶斯估计图像去噪问题．利用先验概率模型一正态反高斯（NIG）分布对图像curvelet系数的稀疏分布进行统计建模，并在此基础上设计出基于NIG的最大后验概率（MAP）估计器．通过估计curvelet子带系数分布的参数，实现基于MAP的子带自适应收缩图像去噪，最后通过仿真验证了去噪算法的性能．结果表明，该方法能有效地去除图像中的噪声，同时较好地保留了图像的纹理和边缘等细节．

英文摘要：

A nonparametric Bayesian estimator for image denoising in the curvelet domain is studied. A prior model, named normal inverse Gaussian （NIG）, is imposed on the curvelet coefficients designed to capture the sparseness of the curvelet expansion. Based on this, a NIG-based maximum a posteriori （MAP） estimator is designed. By estimating the model parameters of curvelet subband coefficients, a MAP-based subband adaptive shrinkage image denoising is realized. Simulation is carried out to show effectiveness of the denoiser. Experimental results show that the proposed method can effectively reduce noise while keep details.