吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (5): 1417-1422.doi: 10.7964/jdxbgxb201405031

• 论文 • 上一篇    下一篇

基于多尺度Meanshift图像去噪算法

赵海英1, 张小利2, 李雄飞2, 彭宏3   

  1. 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
  • 收稿日期:2013-07-04 出版日期:2014-09-01 发布日期:2014-09-01
  • 通讯作者: 彭宏(1972),男,副教授,硕士.研究方向:文化遗产数字化与研发.E-mail:ph_yn@163.com
  • 作者简介:赵海英(1972), 女, 副教授, 博士.研究方向:图形图像处理, 虚拟现实与文化遗产.E-mail:zhaohaiying@bupt.edu.cn
  • 基金资助:

    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).

Image denoising algorithm based on multi-scale Meanshift

ZHAO Hai-ying1, ZHANG Xiao-li2, LI Xiong-fei2, PENG Hong3   

  1. 1.Digital Culture and New Media Technology Research Center,Century College,Beijing University of Posts and Telecommunications, Beijing 102101, China;
    2.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    3.College of Network Education, Xinjiang Normal University, Urumqi 830054, China
  • Received:2013-07-04 Online:2014-09-01 Published:2014-09-01

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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。

关键词: 计算机应用, Meanshift算法, 未知噪声类型, 图像噪声去除

Abstract:

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.

Key words: computer application, Meanshift algorithm, unknown noise type, image noise removal

中图分类号: 

  • TP391
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