中文摘要：

针对含有丰富纹理和边缘特征的噪声图像，提出一种基于形态成分分析（MCA）和Contourlet变换的自适应阈值图像去噪方法．该方法首先引入MCA将噪声图像分为低频部分和高频部分，在此基础上设计一种自适应的分层阈值估计处理策略．根据噪声的分布特性，通过阈值估计和Contourlet变换对噪声图像的低频部分和高频部分进行分频带去噪处理，有效去除噪声图像中的噪声．通过对噪声图像的仿真实验表明，文中方法能较好地保留图像纹理和边缘，并且去噪效果优于传统的均值滤波去噪、中值滤波去噪、小波多层阈值去噪和轮廓波多层阈值去噪方法．

英文摘要：

Aiming at the noise image with rich texture and edge feature, an adaptive thresholding image denoising method based on morphological component analysis （MCA） and contourlet transform is proposed. Firstly, MCA method is introduced to separate the image into the low frequency part and the high frequency part. Then, an adaptive thresholding processing method is designed. Finally, according to the characteristics of noise distribution, the threshold estimation and contourlet transform are used in the low frequency part and the high frequency part to effectively remove the noise from the noisy image. The experimental results on noise images illustrate that the proposed method reserves better textures and edges of the image, and its denoising performance is better than that of the mean filter, the median filter, the wavelet multilevel threshold denoising and the contourlet multilevel threshold denoising.