Optimal sparse representations for blind source separation and blind deconvolution: A learning approach

Michael M. Bronstein; Alexander M. Bronstein; Michael Zibulevsky; Yehoshua Y. Zeevi

doi:10.1109/ICIP.2004.1421428

Optimal sparse representations for blind source separation and blind deconvolution: A learning approach

Michael M. Bronstein^*, Alexander M. Bronstein, Michael Zibulevsky, Yehoshua Y. Zeevi

^*Corresponding author for this work

Technion-Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

We present a generic approach, which allows to adapt sparse blind deconvolution and blind source separation algorithms to arbitrary sources. The key idea is to bring the problem to the case in which the underlying sources are sparse by applying a sparsifying transformation on the mixtures. We present simulation results and show that such transformation can be found by training. Properties of the optimal sparsifying transformation are highlighted by an example with aerial images.

Original language	English
Title of host publication	2004 International Conference on Image Processing, ICIP 2004
Pages	1815-1818
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2004.1421428
State	Published - 2004
Externally published	Yes
Event	2004 International Conference on Image Processing, ICIP 2004 - , Singapore Duration: 18 Oct 2004 → 21 Oct 2004

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	3
ISSN (Print)	1522-4880

Conference

Conference	2004 International Conference on Image Processing, ICIP 2004
Country/Territory	Singapore
Period	18/10/04 → 21/10/04

Access to Document

10.1109/ICIP.2004.1421428

Cite this

Bronstein, M. M., Bronstein, A. M., Zibulevsky, M., & Zeevi, Y. Y. (2004). Optimal sparse representations for blind source separation and blind deconvolution: A learning approach. In 2004 International Conference on Image Processing, ICIP 2004 (pp. 1815-1818). (Proceedings - International Conference on Image Processing, ICIP; Vol. 3). https://doi.org/10.1109/ICIP.2004.1421428

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title = "Optimal sparse representations for blind source separation and blind deconvolution: A learning approach",

abstract = "We present a generic approach, which allows to adapt sparse blind deconvolution and blind source separation algorithms to arbitrary sources. The key idea is to bring the problem to the case in which the underlying sources are sparse by applying a sparsifying transformation on the mixtures. We present simulation results and show that such transformation can be found by training. Properties of the optimal sparsifying transformation are highlighted by an example with aerial images.",

author = "Bronstein, {Michael M.} and Bronstein, {Alexander M.} and Michael Zibulevsky and Zeevi, {Yehoshua Y.}",

year = "2004",

doi = "10.1109/ICIP.2004.1421428",

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booktitle = "2004 International Conference on Image Processing, ICIP 2004",

note = "2004 International Conference on Image Processing, ICIP 2004 ; Conference date: 18-10-2004 Through 21-10-2004",

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Bronstein, MM, Bronstein, AM, Zibulevsky, M & Zeevi, YY 2004, Optimal sparse representations for blind source separation and blind deconvolution: A learning approach. in 2004 International Conference on Image Processing, ICIP 2004. Proceedings - International Conference on Image Processing, ICIP, vol. 3, pp. 1815-1818, 2004 International Conference on Image Processing, ICIP 2004, Singapore, 18/10/04. https://doi.org/10.1109/ICIP.2004.1421428

Optimal sparse representations for blind source separation and blind deconvolution: A learning approach. / Bronstein, Michael M.; Bronstein, Alexander M.; Zibulevsky, Michael et al.
2004 International Conference on Image Processing, ICIP 2004. 2004. p. 1815-1818 (Proceedings - International Conference on Image Processing, ICIP; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Optimal sparse representations for blind source separation and blind deconvolution: A learning approach

Abstract

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