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

M. El-Sayed Waheed, Mohamed-H Mousa and Mohamed-K Hussein
Additional contact information
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

International Journal of System Dynamics Applications (IJSDA), 2013, vol. 2, issue 2, 55-65

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.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/ijsda.2013040104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jsda00:v:2:y:2013:i:2:p:55-65

Access Statistics for this article

International Journal of System Dynamics Applications (IJSDA) is currently edited by Ahmad Taher Azar

More articles in International Journal of System Dynamics Applications (IJSDA) from IGI Global
Bibliographic data for series maintained by Journal Editor ().