Blind deconvolution of timely correlated sources by gradient descent search

In multichannel blind deconvolution (MBD) the goal is to calculate possibly scaled and delayed estimates of source signals from their convoluted mixtures, using approximate knowledge of the source characteristics only. Nearly all of the solutions to MBD proposed so far require from source signals to be pair-wise statistically independent and to be timely not correlated. In practice, this can only be satisfied by specific synthetic signals. In this paper we describe how to modify gradient-based iterative algorithms in order to perform the MBD task on timely correlated sources. Implementation issues are discussed and specific tests on synthetic and real 2-D images are documented.

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