基于Contourlet变换和改进NeighShink的图像去噪  

Image de-nosing based on Contourlet transform and improved NeighShink

作　　者：张雷[1,2] 康宝生[1] 李洪安[1] 

机构地区：[1]西北大学信息科学与技术学院,西安710127 [2]运城学院公共计算机教学部,山西运城044000

出　　处：《计算机应用研究》2014年第4期1267-1269,共3页Application Research of Computers

基　　金：国家自然科学基金资助项目(61272286)

摘　　要：为了有效去除图像噪声且能更好地保护图像细节,提出一种基于Contourlet变换和改进NeighShink的图像去噪方法。首先将图像进行Contourlet变换,利用stein无偏风险估计在各尺度各方向子带上进行启发式阈值估计;然后根据邻域窗能量将低能量系数置0,高能量系数采用近似最大似然估计法估计其方差,再用最小均方误差准则得到真实系数的估计;最后进行邻域系数收缩。实验表明,该方法能有效地去除图像中的噪声,获得更高的峰值信噪比,并且图像的边缘细节得到很好的保护。In order to eliminate the noise in the image effectively and to protect the image detail better, this paper proposed a new method for image denosing based on Contourlet transform and improved NeighShink. It used the stein unbiased risk estima- ting in the directional subband of each scale for heuristic estimating after Contourlet transform of image, then according to neigh- boring window energy, set the low energy coefficient to O. It used approximate maximum likelihood estimation method to estimate the variance of the high energy coefficient, after then used a minimum mean square error criterion to get real coefficient esti- mates. Finally it carried on the neighboring coefficient shrinkage. Experiment on image denoising shows that the method can eliminate the noise in image effectively and get a higher peak signal to noise ratio, and the image detail can be well protected.

关 键 词：CONTOURLET变换 邻域收缩 图像去噪 无偏风险估计 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]