23 June 2003 Blind separation of mixed images using multiscale transforms
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Proceedings Volume 5150, Visual Communications and Image Processing 2003; (2003); doi: 10.1117/12.507216
Event: Visual Communications and Image Processing 2003, 2003, Lugano, Switzerland
Abstract
It was previously shown that sparse representations can improve and simplify the estimation of an unknown mixing matrix of a set of images and thereby improve the quality of separation of source images. Here we propose a multiscale approach to the problem of blind separation of images from a set of their mixtures. We take advantage of the properties of multiscale transforms such as wavelet packets and decompose signals and images according to sets of local features. The resulting partial representations on a tree of data structure depict various degrees of sparsity. We show how the separation error is affected by the sparsity of the decomposition coefficients, and by the misfit between the prior, formulated in accordance with the probabilistic model of the coefficients' distribution, and the actual distribution of the coefficients. Our error estimator, based on the Taylor expansion of the quasi Log-Likelihood function, is used in selection of the best subsets of coefficients, utilized in turn for further separation. The performance of the proposed method is assessed by separation of noise-free and noisy data. Experiments with simulated and real signals and images demonstrate significant improvement of separation quality over previously reported results.
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Pavel Kisilev, Michael Zibulevsky, Yehoshua Y. Zeevi, "Blind separation of mixed images using multiscale transforms", Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); doi: 10.1117/12.507216; https://doi.org/10.1117/12.507216
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KEYWORDS
Error analysis

Wavelets

Transform theory

Independent component analysis

Signal to noise ratio

Associative arrays

Chemical elements

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