基于多尺度Meanshift图像去噪算法  

Image denoising algorithm based on multi-scale Meanshift

作　　者：赵海英[1] 张小利[2] 李雄飞[2] 彭宏[3] 

机构地区：[1]北京邮电大学世纪学院移动媒体与文化计算北京市重点实验室,北京102101 [2]吉林大学计算机科学与技术学院,长春130012 [3]新疆师范大学网络教育学院,乌鲁木齐830054

出　　处：《吉林大学学报（工学版）》2014年第5期1417-1422,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(61163044);'973'国家重点基础研究发展前期计划项目(2010CB334709);新疆科技支疆基金项目(201191215)

摘　　要：针对图像噪声类型未知、Meanshift平滑窗口难以确定致使图像细节被模糊的问题,提出多尺度Meanshift图像去噪算法。结合小波的'数字显微镜'的优点与Meanshift较强无参概率密度估计及快速模板匹配的特点,非常有效地去除了一组实际夜间远程拍摄图像中的未知噪声。算法执行过程中,首先,将图像进行二维离散小波变换,分解出低频子图和承载细节的高频轮廓子图;然后,区别于传统处理方式,高频子图保护不变,对低频子图进行Mean shift分析窗平滑,最后合成高频子图与低频滤波后图像形成去噪声后图像。该方法不仅弥补了单一Meanshift算法由于平滑窗口难以确定致使图像细节被过滤的缺陷,而且解决了一类实拍高噪声图像的去除,信噪比SNR为34.29。结果表明:本文提出的算法可以去除不同类型噪声图像,并可得到较高的信噪比。With unknown types of image noise, it is difficult to determine the Meanshift smooth window, which leads the details of image to be blurry. To overcome this problem, a multi-scale Meanshift algorithm of image denoising is proposed. This algorithm combines the advantages of＂ digital microscope＂ of Wavelet and the characteristics of Meanshift of non-parametric probability density estimation and rapid template matching. So it is very efficient to remove the unknown noise of a group of actual distance image at night. In the implementation of the algorithm, first, the image is carried out two-dimensional discrete Wavelet transform, and the low frequency sub-image and the detailed high frequency sub-band are decomposed. Then, different from traditional process, high frequency sub-image is kept unchanged, and the smooth algorithm is implemented on the low frequency sub-image. Finally, the noise is removed based on the reconstruction of the decomposed sub-images. The algorithm not only makes up for the defect of the single Meanshift algorithm, which is difficult to determine the smooth window, leading to the image details be filtered, but also solves the denoising problem on a group of actual distance images at night, whose Signal-to-Noise Ratio （SNR） is 34.29. Experiment results show that the proposed algorithm has higher ability to remove noise, and gets a higher SNR.

关 键 词：计算机应用 MEANSHIFT算法 未知噪声类型 图像噪声去除 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]