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基于二維小波變換的獨(dú)立分量分析方法及其在圖像分離中的應(yīng)用

王明祥,  方勇,  胡海平

王明祥, 方勇, 胡海平. 基于二維小波變換的獨(dú)立分量分析方法及其在圖像分離中的應(yīng)用[J]. 電子與信息學(xué)報(bào), 2006, 28(3): 471-475.
引用本文: 王明祥, 方勇, 胡海平. 基于二維小波變換的獨(dú)立分量分析方法及其在圖像分離中的應(yīng)用[J]. 電子與信息學(xué)報(bào), 2006, 28(3): 471-475.
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.
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.

基于二維小波變換的獨(dú)立分量分析方法及其在圖像分離中的應(yīng)用

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

  • 摘要: 該文提出了一種新的基于二維小波變換的獨(dú)立分量分析方法。研究表明，當(dāng)各個(gè)源信號(hào)的概率密度分布相同時(shí)，自然梯度算法的穩(wěn)態(tài)誤差與源信號(hào)峭度的平方成反比。因此，對(duì)峭度更大的小波域高頻子圖像進(jìn)行獨(dú)立分量分析可以獲得更高的分離精度。同時(shí)，高頻子圖像的大小為源圖像的1/4，計(jì)算量大大減小，因此算法收斂的速度更快。最后，將該方法用于混合圖像的盲分離，通過(guò)一系列實(shí)驗(yàn)，證實(shí)該方法是有效的。
    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.
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  • 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]楊行峻, 鄭君里. 人工神經(jīng)網(wǎng)絡(luò)與盲信號(hào)處理. 北京：清華大學(xué)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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出版歷程
  • 收稿日期:  2004-08-23
  • 修回日期:  2005-01-03
  • 刊出日期:  2006-03-19

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