基于NSCT子带自适应Bayes阈值图像去噪方法  

Image de-noising algorithm using adaptive Bayes threshold by subband based on NSCT

作　　者：欧阳洪波[1,2] 全惠敏[1] 唐渊[2] 曾业战[3] 

机构地区：[1]湖南大学电气与信息工程学院,湖南长沙410082 [2]湖南工业大学电气与信息工程学院,湖南株洲412007 [3]湖南理工学院物理与电子学院,湖南岳阳414000

出　　处：《电子设计工程》2011年第23期185-188,共4页Electronic Design Engineering

摘　　要：利用非下采样Contourlet变换(NSCT)平移不变性、多分辨率、多方向的优点,提出了一种基于非下采样Con-tourlet变换的子带自适应Bayes阈值图像去噪算法。该算法将源图像分解至NSCT变换域,能根据不同尺度、不同方向的子带能量,自适应调整去噪阈值。实验表明,与Contourlet多尺度阈值去噪、Contourlet自适应阈值去噪相比,该算法在图像去噪上能获得更好的视觉效果和更高的峰值信噪比。Utilizing the nonsubsampled contourlet transform＇s （NSCT） advantages of translation invariance,multi-resolution, multidirection, a image de-noising algorithm using adaptive bayes threshold by subband based on nonsubsampled contourlet transform is presented. Source images are decomposed to the domain of the NSCT, and can adjust denosing threshold adaptively according to different scales and directions of sub-band energy. Comparing with the multi-scale threshold using contourlet transform and using adaptive threshold based on contourlet transform, the simulation results show that the performance of this method is superior in Visual effects and PSNR.

关 键 词：图像去噪 非下采样CONTOURLET变换 Bayes阈值 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]