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

RICH HTML

0   

PDF (PC)

1208

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
[1] Liu Y L, Wang J, Chen X, et al. A robust and f ast non local means algorithm for image denoising[J]. Journal of Computer Science and Technology, 2008, 23(2): 270-279.
[2] Huang T S, Yang G J, Tang G Y. A fast two dimensional median filtering algorithm[J] . IEEE Transactions on Acoustics, Speech and Signal Processing, 1979, 27(1) : 13-18.
[3] Tomasi C, Manduchi R. Bilateral filtering for gray and color images[C]∥Proceedings of the 6th International Conference on Computer Vision, Bombay, 1998: 839-846.
[4] Jwo D J, Wang S H. Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation[J]. IEEE Sensors Journal, 2007,7(5): 778-789.
[5] 王大凯,侯榆青,彭进业. 图像处理的偏微分方程方法[M].北京: 科学出版社,2008.
[6] Chan T F, Osher S, Jiang Shen. The digital TV filter and nonlinear denoising[J]. IEEE Trans on Image Processing, 2001,10(2): 231-241.
[7] Olsen S I. Estimation of noise in images: an evaluation[J].Graphical Models and Image Processing, 1993, 55(4): 319-323.
[8] Amer A, Dubois E. Fast and reliable structure oriented video noise estimation[J]. IEEE Transactions on Circuits and System for Video Technology, 2005, 15(1) : 113-118.
[9] Donoho D L, Johnstone I M. Ideal spatia l adaptation via wavelet shrinkage[J]. Biometrika, 1994, 81: 425-455.
[10] Mallat S G. A theory for multiresolution signal decomposition: a wavelet representation[J] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693.
[11] 朱磊, 徐佩霞, 何佳.一种高效稳健的差分视频噪声估计算法[J] . 数据采集与处理, 2006, 21(3): 354-358.Zhu Lei,Xu Pei-xia,He Jia. Fast and reliable differential video noise estimation algorithm[J] . Journal of Data Acquisition & Processing, 2006, 21(3): 354-358.
[12] Levinbook Y, Wong T F. Restricted risk Bayes linear state estimation[J]. IEEE Transactions on Information Theory, 2009, 55(10):4761-4776.
[13] 孙小炜,李言俊,陈义.基于小波多尺度分解的Mean Shift 图像滤波方法[J]. 计算机工程与应用, 2008,44(18):17-20.Sun Xiao-wei, Li Yan-jun, Chen Yi. Image mean shift filtering method based on wavelet multi-resolution decomposition[J].Computer Engineering and Applications, 2008,44(18):17-20.
[1] 刘富,宗宇轩,康冰,张益萌,林彩霞,赵宏伟. 基于优化纹理特征的手背静脉识别系统[J]. 吉林大学学报(工学版), 2018, 48(6): 1844-1850.
[2] 王利民,刘洋,孙铭会,李美慧. 基于Markov blanket的无约束型K阶贝叶斯集成分类模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1851-1858.
[3] 金顺福,王宝帅,郝闪闪,贾晓光,霍占强. 基于备用虚拟机同步休眠的云数据中心节能策略及性能[J]. 吉林大学学报(工学版), 2018, 48(6): 1859-1866.
[4] 赵东,孙明玉,朱金龙,于繁华,刘光洁,陈慧灵. 结合粒子群和单纯形的改进飞蛾优化算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1867-1872.
[5] 刘恩泽,吴文福. 基于机器视觉的农作物表面多特征决策融合病变判断算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1873-1878.
[6] 欧阳丹彤, 范琪. 子句级别语境感知的开放信息抽取方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1563-1570.
[7] 刘富, 兰旭腾, 侯涛, 康冰, 刘云, 林彩霞. 基于优化k-mer频率的宏基因组聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1593-1599.
[8] 桂春, 黄旺星. 基于改进的标签传播算法的网络聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1600-1605.
[9] 刘元宁, 刘帅, 朱晓冬, 陈一浩, 郑少阁, 沈椿壮. 基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1606-1613.
[10] 车翔玖, 王利, 郭晓新. 基于多尺度特征融合的边界检测算法[J]. 吉林大学学报(工学版), 2018, 48(5): 1621-1628.
[11] 赵宏伟, 刘宇琦, 董立岩, 王玉, 刘陪. 智能交通混合动态路径优化算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[12] 黄辉, 冯西安, 魏燕, 许驰, 陈慧灵. 基于增强核极限学习机的专业选择智能系统[J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[13] 傅文博, 张杰, 陈永乐. 物联网环境下抵抗路由欺骗攻击的网络拓扑发现算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[14] 曹洁, 苏哲, 李晓旭. 基于Corr-LDA模型的图像标注方法[J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.
[15] 侯永宏, 王利伟, 邢家明. 基于HTTP的动态自适应流媒体传输算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1244-1253.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发