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Independent Component Analysis for Image Recovery Using SOMBased Noise Detection
Xiaowei ZHANG Nuo ZHANG Jianming LU Takashi YAHAGI
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E90A
No.6
pp.11251132 Publication Date: 2007/06/01
Online ISSN: 17451337
DOI: 10.1093/ietfec/e90a.6.1125
Print ISSN: 09168508 Type of Manuscript: PAPER Category: Digital Signal Processing Keyword: fixedpoint algorithm, Gaussian momentsbased fixedpoint algorithm, image recovery, independent component analysis (ICA), noise detection, selforganizing map (SOM),
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Summary:
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 selforganizing 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 (fixedpoint algorithm and Gaussian momentsbased fixedpoint algorithm) to separate the images. The fixedpoint algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian momentsbased fixedpoint algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixedpoint or the Gaussian momentsbased fixedpoint 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.

