一种剪切波域的稀疏分量分析方法

doi:10.3969/j.issn.1001-2400.2014.01.009

J4 ›› 2014, Vol. 41 ›› Issue (1): 45-52+146.doi: 10.3969/j.issn.1001-2400.2014.01.009

一种剪切波域的稀疏分量分析方法

纪建;李晓

(西安电子科技大学计算机学院，陕西西安 710071)

收稿日期:2012-09-28 出版日期:2014-02-20 发布日期:2014-04-02
通讯作者: 纪建
作者简介:纪建(1971-)，女，副教授，博士， E-mail: jji@xidian.edu.cn．
基金资助:
国家自然科学基金资助项目(61101248，61070143)；陕西省科技计划资助项目(2011K06-39)

Method for sparse component analysis in the shearlet domain

JI Jian;LI Xiao

(School of Computer Science and Technology, Xidian Univ., Xi'an 710071， China)

Received:2012-09-28 Online:2014-02-20 Published:2014-04-02
Contact: JI Jian

摘要/Abstract

摘要：

在图像盲源分离应用中，传统独立分量分析(ICA)方法直接使用图像混合源作为输入求得解混矩阵，没有利用图像在变换域的稀疏特性，无法获得较好的解混效果．针对这一问题，笔者在分析剪切波具有良好稀疏表示能力的基础上，提出了一种剪切波域的稀疏分量分析方法．该方法首先将图像混合源变换到剪切波域，得到剪切波系数，接着使用峭度选择最稀疏系数，最后将稀疏系数作为ICA方法的输入实现图像分离．由于选择较少的稀疏系数，问题的求解复杂度有了显著的降低．实验结果表明，与传统ICA方法相比，该方法获得了更好的分离效果，且缩短了算法的运行时间．

关键词: 盲源分离, 独立分量分析, 稀疏分量分析, 剪切波变换

Abstract:

In applications of the image blind source separation, the traditional method of Independent Component Analysis(ICA) computes the mixed matrix by using source image directly, without using the prior knowledge that images can be represented sparsely in the transform domain, and it can not lead to a better effect. Based on the capacity of image sparse representation by shearlet, a method of sparse component analysis in the shearlet domain is presented. The image mixed source is first transformed to the shearlet domain and obtains a shearlet coefficient, then the sparsest coefficient is selected by computing kurtosis, and finally the sparse coefficient is used as the input of the ICA method to realize image separation. The complexity of the solving procedure represents a significant decrease since it chooses a less sparse coefficient. Experimental results show that, compared with the traditional ICA method, the method in this paper leads to a better separation effect and shortens the operation time of the algorithm.

Key words: blind source separation, independent component analysis, sparse component analysis, shearlet transform

纪建;李晓. 一种剪切波域的稀疏分量分析方法[J]. J4, 2014, 41(1): 45-52+146.

JI Jian;LI Xiao. Method for sparse component analysis in the shearlet domain[J]. J4, 2014, 41(1): 45-52+146.

参考文献

［1］ Karray E, Loghmari M A, Naceur M S. Blind Source Separation of Hyperspectral Images in DCT-domain ［C］//Advanced Satellite Multimedia Systems Conference and the 11th Signal Processing for Space Communications Workshop. Piscataway: IEEE, 2010: 381-388.
［2］ Bronstein A M, Bronstein M M, Zibulevsky M, et al. Sparse ICA for Blind Separation of Transmitted and Reflected Images ［J］. International Journal of Imaging Systems and Technology, 2005, 15(1): 84-91.
［3］刘丹华, 石光明, 周佳社. 一种冗余字典下的信号稀疏分解新方法［J］. 西安电子科技大学学报, 2008, 35(2): 228-232.
Liu Danhua, Shi Guangming, Zhou Jiashe. New Method for Signal Sparse Decomposition over a Redundant Dictionary ［J］. Journal of Xidian University, 2008, 35(2): 228-232.
［4］ Maazaoui M, Abed Meraim K, Grenier Y. Adaptive Blind Source Separation with HRTFs Beamforming Preprocessing ［C］//Sensor Array and Multichannel Signal Processing Workshop. Washington DC: IEEE, 2012: 269-272.
［5］ Kim Y, Nadar M S, Bilgin A. Wavelet-based Compressed Sensing Using a Gaussian Scale Mixture Model ［J］. IEEE Transactions on Image Processing, 2012, 21(6): 3102-3108.
［6］ Fowler J E, Mun S, Tramel E W. Multiscale Block Compressed Sensing with Smoothed Projected Landweber Reconstruction ［C］//Proceedings of the European Signal Processing Conference. Poland: European Signal Processing Conference, 2011: 564-568.
［7］王书振, 邹子健, 李莉, 等. 一种基于稀疏正则化的图像盲复原方法［J］. 西安电子科技大学学报, 2012, 39(6): 167-169.
Wang Shuzhen, Zhou Zijian, Li Li, et al. Method for Blind Image Restoration Based on Sparse Regularization ［J］. Journal of Xidian University, 2012, 39(6): 167-169.
［8］赵知劲, 解婷婷, 李小平, 等. 一种小波域盲源分离算法［J］. 西安电子科技大学学报, 2007, 34(3): 423-427.
Zhao Zhijin, Xie Tingting, Li Xiaoping. Blind Source Separation Method in the Wavelet Domain ［J］. Journal of Xidian University, 2007, 34(3): 423-427.
［9］ Yang Yang, Li Zuoli, Wang Xiuqin, et al. Noise Source Separation Based on the Blind Source Separation ［C］//Proceedings of Chinese Control and Decision Conference. Piscataway: IEEE, 2011: 2236-2240.
［10］ Bobin J, Starck J L, Fadili J, et al. Sparsity and Morphological Diversity in Blind Source Separation ［J］. IEEE Transactions on Image Processing, 2007, 16(1): 2662-2674.
［11］ Guo K, Labate D. Optimally Sparse Multidimensional Representation Using Shearlets ［J］. SIAM Journal on Mathematical Analysis, 2007, 39(1): 298-318.
［12］张瑾, 方勇. 基于分块Contourlet变换的图像独立分量分析方法［J］. 电子与信息学报, 2007, 29(8): 1813-1816.
Zhang Jin, Fang Yong. An Independent Component Analysis Algorithm Based on Block-wise Contourlet Transform ［J］. Journal of Electronics and Information Technology, 2007, 29(8): 1813-1816.
［13］ Selesnick I W, Baraniuk R G, Kingsbury N G. The Dual-tree Complex Wavelet Transform ［J］. IEEE Signal Processing Magazine, 2005, 22(6): 123-151.
［14］ Cardoso J F. Infomax and Maximum Likelihood for Source Separation ［J］. IEEE Signal Processing Letters, 1997, 4(4): 112-114.
［15］ Karhunen J. Neural Approaches to Independent Component Analysis and Source Seperation［C］//Proceedings of European Symposium on Artificial Neural Networks. Brussels: DFacto, 1996: 249-266.

[1]	赵伟,侴胜男,杨烁,李雄飞. 采用盲源分离的扩频通信抗干扰算法[J]. 西安电子科技大学学报, 2020, 47(1): 73-79.
[2]	王川川,曾勇虎,付卫红,汪连栋. 一种最大密度检测欠定混合矩阵估计算法[J]. 西安电子科技大学学报, 2019, 46(1): 106-111.
[3]	周志文;黄高明;高俊. 广义生成函数张量分解的欠定混合盲辨识[J]. 西安电子科技大学学报, 2016, 43(5): 116-120.
[4]	付卫红;王璐;马丽芬. 一种改进的势函数欠定盲源分离算法[J]. J4, 2014, 41(6): 1-5+88.
[5]	李钊;杨家玮;姚俊良;陈婷. 采用独立分量分析的多用户MIMO下行传输策略[J]. J4, 2010, 37(2): 192-196.
[6]	王法松1;3;李宏伟1;李睿2. 非参数GKNN估计的高效独立成分分析算法 [J]. J4, 2008, 35(4): 764-768.
[7]	刘建强;冯大政. 一种按块递归的盲源分离方法 [J]. J4, 2008, 35(2): 233-236.
[8]	张华;冯大政;聂卫科;徐先峰. 非正交联合对角化盲源分离算法 [J]. J4, 2008, 35(1): 27-317.
[9]	孙永军;易克初. 利用核方法的直扩系统多个窄带干扰抑制 [J]. J4, 2007, 34(4): 554-557.
[10]	赵知劲1;2;解婷婷2;李小平3;赵治栋2. 一种小波域盲源分离算法 [J]. J4, 2007, 34(3): 423-427.
[11]	李小军1;楼顺天1;张贤达2. 基于估计概率密度函数的独立分量分析方法[J]. J4, 2005, 32(4): 574-578.
[12]	李小军1;楼顺天1;张贤达2. 一种针对复值信号的独立分量分析方法[J]. J4, 2005, 32(3): 447-451.
[13]	张军英1;2;刘利平1. 基于部分独立分量分析的盲源分离[J]. J4, 2004, 31(3): 334-337.
[14]	李小军1;朱孝龙2;张贤达2. 盲信号分离研究分类与展望[J]. J4, 2004, 31(3): 399-404.
[15]	暂时无作者信息. 盲信源分离中信源动态变化的识别[J]. J4, 2003, 30(5): 598-603.

一种剪切波域的稀疏分量分析方法

Method for sparse component analysis in the shearlet domain

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0