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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication2004 International Conference on Image Processing, ICIP 2004
Pages1815-1818
Number of pages4
DOIs
StatePublished - 2004
Externally publishedYes
Event2004 International Conference on Image Processing, ICIP 2004 - , Singapore
Duration: 18 Oct 200421 Oct 2004

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume3
ISSN (Print)1522-4880

Conference

Conference2004 International Conference on Image Processing, ICIP 2004
Country/TerritorySingapore
Period18/10/0421/10/04

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