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Proceedings Paper

Metropolis Monte Carlo deconvolution
Author(s): Abolfazl M. Amini
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Paper Abstract

Metropolis Monte Carlo deconvolution is introduced. The actual input data is reconstructed by means of grains according to a probability distribution function defined by the blurred data. As the blurred data is being reconstructed a grain is place in the actual input domain at every or a finite number of reconstruction steps. To test the method a wide Gaussian Impulse Response Function is designed and convolved with an input data set containing 24 points. As the grain size (GS) is reduced the number of Monte Carlo moves and with it the accuracy of the method is increased. The grain sizes ranging from 0.0001 to 1.0 are used. For each GS five different random number seeds are used for accuracy. The mean-square error is calculated and the average MSE is plotted versus the GS. Sample reconstructed functions are also given for each GS.

Paper Details

Date Published: 21 July 1999
PDF: 11 pages
Proc. SPIE 3716, Visual Information Processing VIII, (21 July 1999); doi: 10.1117/12.354714
Show Author Affiliations
Abolfazl M. Amini, Southern Univ. (United States)


Published in SPIE Proceedings Vol. 3716:
Visual Information Processing VIII
Stephen K. Park; Richard D. Juday, Editor(s)

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