摘要
在小波半软阈值图像去噪方法基础上,提出了一种基于自适应局部相关系数的新方法。该方法在软阈值法和硬阈值法之间有很好的折衷,通过加入局部相关系数,使其在各种小波变换中均能增强子带内小波系数的相关性。在阈值选取中选用了基于Bayes风险估计的自适应阈值和具有统计意义上的阈值方法,获得了小波系数不同子带不同方向的最优估计。实验结果显示,该方法去噪效果显著,同时能够改善小波变换所造成的图像视觉失真和边缘振荡效应,更好地保留了图像边缘和细节纹理特征。该方法可通过调节局部相关系数控制图像去噪程度和效果,能满足不同需求,具有很高的实用价值。
Based on the image denoising method of wavelet semi-soft threshold,a novel method based on adaptive local correlation coefficient is proposed.By introducing the local correlation coefficient,the proposed method,which has a good compromise between the soft threshold and hard threshold,can enhance the sub band correlation among wavelet coefficients in a variety of wavelet transforms.The adap tive threshold is selected based on Bayes risk estimation and the statistical significance in order to achieve the best estimation of wavelet coefficients in the different directions and subbands.Experimental results show that the new method improves the image denoising effect efficiently,and reduces the image visual distortion and edge oscillation caused by the image wavelet transform so as to retain the image feature of edges and detail at the same time.This method can be controlled by adjusting the partial correlation co efficients with the extent and effect of image denoising,thus meeting the different needs and having high practical value.
出处
《计算机工程与科学》
CSCD
北大核心
2014年第8期1566-1570,共5页
Computer Engineering & Science
基金
国家自然科学基金资助项目(61175029)
