Sparsity and Morphological Diversity in Blind Source Separation

Over the last few years, the development of multichannel sensors motivated interest in methods for the coherent processing of multivariate data. Some specific issues have already been addressed as testified by the wide literature on the so-called blind source separation (BSS) problem. In this contex...

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Vydáno v:	IEEE transactions on image processing Ročník 16; číslo 11; s. 2662 - 2674
Hlavní autoři:	Bobin, J., Starck, J.-L., Fadili, J., Moudden, Y.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.11.2007 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Applied sciences Blind source separation Blind source separation (BSS) Blinds Coherence Computer Science Convergence curvelets Detection, estimation, filtering, equalization, prediction Exact sciences and technology Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image Processing Independent component analysis Information Storage and Retrieval - methods Information, signal and communications theory Mathematical models Miscellaneous morphological diversity Mutual information overcomplete representations Representations Reproducibility of Results Robustness Sensitivity and Specificity Separation Signal analysis Signal and communications theory Signal processing Signal processing algorithms Signal representation. Spectral analysis Signal representations Signal, noise Size measurement Source separation Sparsity Telecommunications and information theory Testing wavelets Performance evaluation Parameter estimation Source separation curvelets Image processing Signal estimation Blind source separation sparsity Blind source separation (BSS) Signal representation Algorithm morphological diversity overcomplete representations Morphological analysis wavelets Signal processing Numerical simulation Robustness Multiple channel
ISSN:	1057-7149, 1941-0042
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Popis
Shrnutí:	Over the last few years, the development of multichannel sensors motivated interest in methods for the coherent processing of multivariate data. Some specific issues have already been addressed as testified by the wide literature on the so-called blind source separation (BSS) problem. In this context, as clearly emphasized by previous work, it is fundamental that the sources to be retrieved present some quantitatively measurable diversity. Recently, sparsity and morphological diversity have emerged as a novel and effective source of diversity for BSS. Here, we give some new and essential insights into the use of sparsity in source separation, and we outline the essential role of morphological diversity as being a source of diversity or contrast between the sources. This paper introduces a new BSS method coined generalized morphological component analysis (GMCA) that takes advantages of both morphological diversity and sparsity, using recent sparse overcomplete or redundant signal representations. GMCA is a fast and efficient BSS method. We present arguments and a discussion supporting the convergence of the GMCA algorithm. Numerical results in multivariate image and signal processing are given illustrating the good performance of GMCA and its robustness to noise.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	1057-7149 1941-0042
DOI:	10.1109/TIP.2007.906256