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IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2007 E90-A(6):1125-1132; doi:10.1093/ietfec/e90-a.6.1125
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Copyright © 2007 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Papers -- Digital Signal Processing

Independent Component Analysis for Image Recovery Using SOM-Based Noise Detection

Xiaowei ZHANG1, Nuo ZHANG2, Jianming LU1 and Takashi YAHAGI1

1 The authors are with the Graduate School of Science and Technology, Chiba University, Chiba-shi, 263–8522 Japan. E-mail: chou{at}graduate.chiba-u.jp, 2 The author is with the Graduate School of Information Systems, University of Electro-Communications, Chofu-shi, 182–8585 Japan.


  Abstract

In this paper, a novel independent component analysis (ICA) approach is proposed, which is robust against the interference of impulse noise. To implement ICA in a noisy environment is a difficult problem, in which traditional ICA may lead to poor results. We propose a method that consists of noise detection and image signal recovery. The proposed approach includes two procedures. In the first procedure, we introduce a self-organizing map (SOM) network to determine if the observed image pixels are corrupted by noise. We will mark each pixel to distinguish normal and corrupted ones. In the second procedure, we use one of two traditional ICA algorithms (fixed-point algorithm and Gaussian moments-based fixed-point algorithm) to separate the images. The fixed-point algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian moments-based fixed-point algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixed-point or the Gaussian moments-based fixed-point algorithm to update the separation matrix. The proposed approach has the capacity not only to recover the mixed images, but also to reduce noise from observed images. The simulation results and analysis show that the proposed approach is suitable for practical unsupervised separation problem.

Key Words: fixed-point algorithm, Gaussian moments-based fixed-point algorithm, image recovery, independent component analysis (ICA), noise detection, self-organizing map (SOM)

Manuscript received June 12, 2006. Manuscript revised November 7, 2006. Final manuscript received March 2, 2007.


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