A Simple Approximation for Fast Nonlinear Deconvolution

  • Jordi Solé-Casals
  • Cesar F. Caiafa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7015)

Abstract

When dealing with nonlinear blind deconvolution, complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing or in microarray data analysis. In this paper we propose a simple method to reduce computational time for the inversion of Wiener systems by using a linear approximation in a minimum-mutual information algorithm. Experimental results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased.

Keywords

Mutual Information Score Function Linear Complexity Nonlinear Distortion Deconvolution Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jordi Solé-Casals
    • 1
  • Cesar F. Caiafa
    • 2
    • 3
  1. 1.Digital Technologies GroupUniversity of VicVicSpain
  2. 2.Instituto Argentino de Radioastronomía (CCT La Plata, CONICET) C.C.5Villa ElisaArgentina
  3. 3.Facultad de IngenieríaUBAAla surArgentina

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