[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(2):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(2):255-260.[doi:10.3969/j.issn.1673-4785.201511015]
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WANG Kejun,YANG Xiaofei.The contourlet denoising algorithm based on modified noise variance estimation[J].CAAI Transactions on Intelligent Systems,2015,10(2):255-260.[doi:10.3969/j.issn.1673-4785.201511015]
采用改进结构特征度度量分析的噪声标准差估计算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
10
期数:
2015年第2期
页码:
255-260
栏目:
学术论文—人工智能基础
出版日期:
2015-04-25
- Title:
- The contourlet denoising algorithm based on modified noise variance estimation
- Keywords:
- image denoising; contourlet; standard deviation estimation of image noise; image structural characteristic measurement; histogram methods
- 分类号:
- TP18;TN911.73
- 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:
-
[1] DONOHO D L, JOHNSTONE L M. Ideal spatial adap-tation by wavelet shrinkage[J]. Biometrika, 1994, 81(3): 425-455.
[2] STARCK J L, EMMANUEL C. The curvelet transform for image denoising[J]. IEEE Transactions on Image Processing, 2002, 11(6): 670-684.
[3] DO M, VETTERLI M. The contourlet transform : an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.
[4] TAI Shenchuan, YANG Shiming. A fast method for image noise estimation using Laplacian operator and adaptive edge detection[C]//Proceedings of the 2008 International Symposium on Communications, Control and Signal Processing. Valetta, Malta, 2008: 1077-1081.
[5] CHEN Yikai, WANG Jingfa. Skew detection and reconstruction based on maximization of variance of transition counts[J]. Pattern Recognition, 2000, 33(2): 195-208.
[6] 满家巨,沈军.一种图像噪声准确估计方法[J].湖南师范大学自然科学学报, 2008, 31( 2): 44-47.MAN Jiaju, SHEN Jun. A method of image noise estimate[J]. Journal of Natural Science of Human Normal University, 2008, 31 (2): 44-47.
[7] BILCU R, VEHVILAINEN M. A new method for noise estimation in images[C]//Proceedings of the 2005 IEEE EURASIP International Workshop on Nonlinear Signal and Image Processing. Sapporo, Japan, 2005: 18-20.
[8] OLSEN S. Estimation of noise in images: an evaluation[J].Graphical Models and Image Processing, 1993, 5(4) : 319-323.
[9] VETTERLI M D M. Contourlets: a directional multiresolution image representation[C]//2002 International Conference on Image Processing. New York, USA, 2002: 357-360.
[10] KIM Y, LEE J. Image feature and noise detection based on statistical independent tests and their applications in image processing[J]. IEEE Trans Consumer Electronics, 2005, 51(4): 1367-1374.
[11] ZHOU ZF, CAO J Z. Contourlet-based image denoising algorithm using adaptive windows[C]//IEEE Conference on Industrial and Applications. Xi’an, China, 2009: 3654-3657.
[12] CHEN G Y, ZHU W P. Image denoising using neighbouring contourlet coefficients[C]//Advances in Neutral Networks. Beijing, China, 2008: 384-391.
[13] LIU Lixiong, LIU Bao, Huang Hua, et al. No-reference image quality assessment based on spatial and spectral entropies[J]. Signal Processing: Image Communication, 2014, 29(8): 856-863.
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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