[1]王科俊,杨晓飞.采用改进结构特征度度量分析的噪声标准差估计算法[J].智能系统学报,2015,10(02):255-260.[doi:10.3969/j.issn.1673-4785.201511015]
 WANG Kejun,YANG Xiaofei.The contourlet denoising algorithm based on modified noise variance estimation[J].CAAI Transactions on Intelligent Systems,2015,10(02):255-260.[doi:10.3969/j.issn.1673-4785.201511015]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年02期
页码:
255-260
栏目:
出版日期:
2015-04-25

文章信息/Info

Title:
The contourlet denoising algorithm based on modified noise variance estimation
作者:
王科俊 杨晓飞
哈尔滨工程大学 自动化学院, 黑龙江 哈尔滨 150001
Author(s):
WANG Kejun YANG Xiaofei
College of Automation, Harbin Engineering University, Harbin 150001, China
关键词:
图像去噪轮廓波图像噪声标准差估计图像结构特征度直方图法
Keywords:
image denoisingcontourletstandard deviation estimation of image noiseimage structural characteristic measurementhistogram methods
分类号:
TP18;TN911.73
DOI:
10.3969/j.issn.1673-4785.201511015
文献标志码:
A
摘要:
提出了一种基于改进的噪声标准差估计的轮廓波去噪算法,在常用的轮廓波去噪算法基础上提出了新的解决方案。该方案将滤波法与改进的图像结构特征度度量分析算法结合起来,筛选出适合计算噪声标准差的图像子块集合,再用直方图法估计图像噪声标准差,然后将该标准差用于轮廓波去噪。在标准差估计对比试验中,将滤波法、分块法、改进的分块法与文中的标准差估计算法进行对比;在去噪对比试验中,采用基本的小波阈值去噪算法(universal 阈值),由小波阈值法引申出的普通轮廓波阈值去噪算法,基于维纳滤波的轮廓波去噪算法,基于系数建模的轮廓波去噪算法与文中算法做对比。实验结果表明:文中算法能够更加精确地估计图像噪声标准差,且去噪效果与普通轮廓波去噪及其他轮廓波去噪算法相比更加稳定,鲁棒性更好。
Abstract:
In this paper, an algorithm based on the modified noise standard deviation estimation was proposed. The proposed algorithm provides a new approach for the foundation of common contourlet denoising methods. The combination of modified image structural characteristic measurement analysis method and filter method is used in selecting the image sub-block, which is suitable for computing standard deviation of noise. Finally, the histograms of those sub-images are used to estimate standard deviation of the image noise, which is subsequently used for denoising of contourlet. In the contrast experiment of standard deviation estimation the filter methods, partition methods and improved partition methods are chosen to compare with the standard deviation estimation algorithm. In the contrast experiment of denoising, the universal wavelet threshold denoising, the common contourlet threshold denoising extended from wavelet threshold, the contourlet denoising based on wiener filtering, and the contourlet denoising based on coefficient models are chosen to compare with the denoising algorithm proposed in this paper. The experimental results showed that the method can estimate standard deviation of images more accurately and performs more reliable than other contourlet denoising algorithms.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-11-13;改回日期:。
基金项目:国家自然科学基金资助项目(61100007).
作者简介:王科俊,男,1962年生,教授,博士生导师,主要研究方向为模糊混沌神经网络、自适应逆控制理论、可拓控制、网络智能控制、模式识别、微小型机器人系统等.发表学术论文300余篇;杨晓飞,男,1985年生,博士研究生,主要研究方向为模式识别、图像处理、超小波,已发表学术论文5篇,EI检索3篇.
通讯作者:杨晓飞.E-mail:licerain@hotmail.com.
更新日期/Last Update: 2015-06-15