OPTIMAL SPARSE REPRESENTATIONS FOR BLIND SOURCE SEPARATION AND BLIND DECONVOLUTION: A LEARNING APPROACH (TP-L2)

Author(s) : Michael Bronstein (Technion - Israel Institute of Technology, Israel) Alexander Bronstein (Technion - Israel Institute of Technology, Israel) Michael Zibulevsky (Technion - Israel Institute of Technology, Israel) Yehoshua Zeevi (Technion - Israel Institute of Technology, Israel)

Abstract : We present an 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.

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