基于二维小波变换的独立分量分析方法及其在图像分离中的应用

王明祥; 方勇; 胡海平

基于二维小波变换的独立分量分析方法及其在图像分离中的应用

计量
- 文章访问数: 2224
- HTML全文浏览量: 91
- PDF下载量: 779
- 被引次数: 0
出版历程
- 收稿日期: 2004-08-23
- 修回日期: 2005-01-03
- 刊出日期: 2006-03-19

ICA Method Based on 2-D Wavelet Transform and Its Application to Image Separation

摘要

摘要: 该文提出了一种新的基于二维小波变换的独立分量分析方法。研究表明，当各个源信号的概率密度分布相同时，自然梯度算法的稳态误差与源信号峭度的平方成反比。因此，对峭度更大的小波域高频子图像进行独立分量分析可以获得更高的分离精度。同时，高频子图像的大小为源图像的1/4，计算量大大减小，因此算法收敛的速度更快。最后，将该方法用于混合图像的盲分离，通过一系列实验，证实该方法是有效的。
- 小波变换;独立分量分析;自然梯度算法;图像分离
Abstract: In this paper, a kind of new Independent Component Analysis (ICA) method based on 2-dimensional wavelet transform is proposed. According to the research, the steady-state error of the Natural Gradient Algorithm (NGA) is inverse proportional to the quadratic of the kurtosis of the sources when the probability distribution function of each source is the same. In addition, the kurtosis of the detail coefficients in wavelet domain is always bigger than that of the original images, so the separation precision of ICA method based on 2-dimensional wavelet transform is higher than that of the traditional ICA method. Furthermore, the size of the sub-image in 2-dimensional wavelet domain is a quarter of the source image, so the convergence speed of the proposed method is faster. Finally, this method is used to separate the mixed images. A set of experiments in different situations is done and the simulation results show that the proposed method is effective.

HTML全文

参考文献(1)

Jutten C, Herault J. Blind separation of sources. Part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 1991, 24(1): 110. .[2]Comon P. Independent component analysis: A new concept? Signal Processing, 1994, 36(3): 287314. .[3]Bell A, Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 1995, 7(6): 11291159. .[4]Amari S, Chen T P, Cichocki A. Stability analysis of learning algorithms for blind source separation[J].Neural Networks.1997,10(8):1345-[5]Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis[J].IEEE Trans. on Neural Networks.1999, 10(3):626-[6]Te-Won Lee, Lewicki M S. Unsupervised image classification, segmentation, and enhancement using ICA mixture models. IEEE Trans. on Image Processing, 2002, 11(3): 270279. .[7]Bartlett M S, Movellan J R, Sejnowski T J. Face recognition by independent component analysis. IEEE Trans. on Neural Networks, 2002, 13(6): 14501464. .[8]Zhang S, Rajan P K. Independent component analysis of digital image watermarking. IEEE International Symposium on Circuits and Systems, Scottsdale, Arizona, 2002, 3: III 217III 220. .[9]杨行峻, 郑君里. 人工神经网络与盲信号处理. 北京：清华大学出版社, 2003, 第六章.[10]Pham D T, Garat P. Blind separation of mixture of independent sources through a quasi-maximum likelihood approach. IEEE Trans. on Signal Processing, 1997, 45(7): 17121725. .[11]Antonini M, Barlaud M, Mathieu P, Daubechies I. Image coding using wavelet transform[J].IEEE Trans. on Image Processing.1992, 1(2):205-[12]Jafari M G, Chambers J A. Wavelet domain natural gradient algorithm for blind source separation of non-stationary sources. Electronics Letters, 2002, 38(14): 759761. .[13]Yang H H, Amari S. Adaptive on-line learning algorithms for blind separation-maximum entropy and minimum mutual information[J].Neural Computation.1997, 9(5):1457-

施引文献

资源附件(0)

访问统计