
Proceedings Paper
Simultaneous optimization by simulation of iterative deconvolution and noise removal for non-negative dataFormat | Member Price | Non-Member Price |
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Paper Abstract
This paper introduces a method by which one can find the optimum iteration numbers for noise removal and
deconvolution of sampled data. The method employs the mean squared error, which is the square of the difference
between the deconvolution result and the input, for optimization. As an example of the iterative methods of noise
removal and deconvolution, the always convergent method of Ioup is used for the simultaneous optimization by
simulation research presented in this paper. This method is applied to achieve optimization for two Gaussian
impulse response functions, one narrow (rapidly converging) and the other wide (slowly converging). The input
function used consists of three narrow peaks selected to give some overlap after convolution with the Gaussian
impulse response function. Normal distributed noise is added to the convolution of the input with the impulse
response function. A range of signal-to-noise ratio is used to optimize the always convergent iterations for both of
these Gaussians. For the narrow Gaussian 15 signal-to-noise ratio cases are studied while for the wide Gaussian 11
signal-to-noise ratios cases are considered. To achieve statistically reliable results 50 noisy data sets are generated
for each signal-to-noise ratio case. For a given signal-to-noise ratio case the optimum deconvolution and noise
removal iteration numbers are found and tabulated. The tabulated results are given in tables one through three. Once
these optimum numbers are found they can be used in an equivalent window in the Fourier transform domain,
although the non-negativity constraint can only be applied in the function domain.
Paper Details
Date Published: 23 May 2013
PDF: 11 pages
Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87451R (23 May 2013); doi: 10.1117/12.2014178
Published in SPIE Proceedings Vol. 8745:
Signal Processing, Sensor Fusion, and Target Recognition XXII
Ivan Kadar, Editor(s)
PDF: 11 pages
Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87451R (23 May 2013); doi: 10.1117/12.2014178
Show Author Affiliations
Abolfazl M. Amini, Southern Univ. and A&M College (United States)
George E. Ioup, Univ. of New Orleans (United States)
George E. Ioup, Univ. of New Orleans (United States)
Juliette W. Ioup, Univ. of New Orleans (United States)
Published in SPIE Proceedings Vol. 8745:
Signal Processing, Sensor Fusion, and Target Recognition XXII
Ivan Kadar, Editor(s)
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