Metropolis Monte Carlo deconvolution

Abolfazl M. Amini

doi:10.1117/12.354714

21 July 1999 Metropolis Monte Carlo deconvolution

Abolfazl M. Amini

Proceedings Volume 3716, Visual Information Processing VIII; (1999) https://doi.org/10.1117/12.354714
Event: AeroSense '99, 1999, Orlando, FL, United States

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.

Citation Download Citation

Abolfazl M. Amini "Metropolis Monte Carlo deconvolution", Proc. SPIE 3716, Visual Information Processing VIII, (21 July 1999); https://doi.org/10.1117/12.354714

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