AN IMPLICIT METHOD FOR DATA PREDICTION AND IMPULSE NOISE REMOVAL FROM CORRUPTED SIGNALS

Zhang de S., Haixiang Wang, Donald J. Kouri and David K. Hoffman
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Zhang de S.: Department of Chemistry and Department of Physics, University of Houston, Houston, TX 77204-5641, USA
Haixiang Wang: Department of Chemistry and Department of Physics, University of Houston, Houston, TX 77204-5641, USA
Donald J. Kouri: Department of Chemistry and Department of Physics, University of Houston, Houston, TX 77204-5641, USA
David K. Hoffman: Department of Chemistry and Ames Laboratory (The Ames Laboratory is operated for the Department of Energy by Iowa State University under Contract No. 2-7405-ENG82.), Iowa State University, Ames, IA 50011, USA

International Journal of Modern Physics C (IJMPC), 2002, vol. 13, issue 04, 565-583

Abstract: A robust and reliable implicit method is proposed for application in data interpolation. The algorithm is based on a recently developed analytic approximation method, namely the distributed approximating functionals (DAFs), which is known to have the "well-tempered" property of UNIFORMLY approximating a function and its derivatives. In comparison with the conventionally used local explicit interpolation algorithms, the implicit method achieves much more accurate interpolation results because it couples all sample values (both known and unknown) in the domain of interest using a set of simultaneous linear algebraic equations. Due to the fact that the well-tempered DAFs also are very good low-pass filters, the performance of the DAF-based implicit method is not affected very much by the high frequency noise in the input signal. As an application, the proposed algorithm is applied to signals that are corrupted with impulse noise.

Keywords: Implicit method; data interpolation and prediction; noise removal; median filter (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1142/S0129183102003322

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