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.
出处
《计算机应用研究》
CSCD
北大核心
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)
