Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Natural gradient works efficiently in learning
Neural Computation
Communications of the ACM
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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.