基于小波域自适应高斯混合模型的图像去噪方法  

Image denoising method based on adaptive Gaussian mixture model in wavelet domain

作　　者：崔艳秋[1] 王珂[1] 孙巍[1] 

机构地区：[1]吉林大学通信工程学院,长春130022

出　　处：《吉林大学学报（工学版）》2006年第6期983-988,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：韩国通信电子研究院合作基金资助项目(12003121192202)

摘　　要：提出了一种基于小波系数局部统计模型的图像去噪方法。该方法利用小波子带的方向性特点以及小波系数尺度内的相关性,将小波系数的概率分布建模为一种自适应高斯混合模型,在贝叶斯框架中采用这种概率模型可以得到一种具有空间自适应性的贝叶斯萎缩函数。利用这种萎缩函数可以实现对小波系数的修正。实验结果表明,利用该方法进行图像去噪能够取得良好的效果,同时可以有效地保留图像的细节。An image denoising method was proposed based on a local statistical model of wavelet coefficients This method modeled the distribution of wavelet coefficients as an adaptive Gaussian mixture model. This model took into account intrascale dependencies between wavelet coefficients and it was adaptive to the wavelet subbands corresponding to three orientations in the image. Based on this model in a Bayesian framework, a spatially adaptive Bayesian shrinkage function was obtained and each modified coefficient was decided separately. Experimental results demonstrate this method improves the denoising performance and preserves the details of the image.

关 键 词：信息处理技术 图像去噪 小波变换 自适应高斯混合模型 

分 类 号：TN919.73[电子电信—通信与信息系统]