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Volume 28 Issue 3
Sep.  2010
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Xi CHEN, Kun ZHANG. A Classifier Learning Method Based on Tree-Augmented Naïve Bayes[J]. Journal of Electronics & Information Technology, 2019, 41(8): 2001-2008. doi: 10.11999/JEIT180886
Citation: Wang Ming-xiang, Fang Yong, Hu Hai-ping. ICA Method Based on 2-D Wavelet Transform and Its Application to Image Separation[J]. Journal of Electronics & Information Technology, 2006, 28(3): 471-475.

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

  • Received Date: 2004-08-23
  • Rev Recd Date: 2005-01-03
  • Publish Date: 2006-03-19
  • 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.
  • 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-
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