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Blind Deconvolution of Sources in Fourier Space Based on Generalized Laplace Distribution

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  • M. El-Sayed Waheed

    (Department of Computer Science, Suez Canal University, Ismailia, Egypt)

  • Mohamed-H Mousa

    (Department of Computer Science, Suez Canal University, Ismailia, Egypt)

  • Mohamed-K Hussein

    (Department of Computer Science, Suez Canal University, Ismailia, Egypt)

Abstract

An approach to multi-channel blind de-convolution is developed, which uses an adaptive filter that performs blind source separation in the Fourier space. The approach keeps (during the learning process) the same permutation and provides appropriate scaling of components for all frequency bins in the frequency space. Experiments indicate that Generalized Laplace Distribution can be used effectively to blind de-convolution of convolution mixtures of sources in Fourier space compared to the conventional Laplacian and Gaussian function.

Suggested Citation

  • M. El-Sayed Waheed & Mohamed-H Mousa & Mohamed-K Hussein, 2013. "Blind Deconvolution of Sources in Fourier Space Based on Generalized Laplace Distribution," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 2(2), pages 55-65, April.
    • Handle: RePEc:igg:jsda00:v:2:y:2013:i:2:p:55-65
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