Performance analysis on Monte Carlo deconvolution decision rules in presence of noise

Abolfazl M. Amini

doi:10.1117/12.351321

Abstract

The performance of the Metropolis Monte Carlo (MMC) and Frieden's Monte Carlo (FMC) deconvolution techniques are compared and analyzed in presence of noise. Two different Gaussian distributed additive noise data sets with Signal- to-noise-ratio (SNR) ranging from 10 to 150 is generated and added to a set of blurred data. The blurred data is obtained by convolving a 24 points input signal that has three peaks with a 21 points wide Gaussian impulse response function. The mean squared error (MSE) is used to compare the two techniques. The MSE is calculated by comparing the reconstructed input signal with the true input signal. The MSEs calculated for each SNR of a given data set is averaged. The averaged MSE for MMC and FMC techniques are potted vs. SNR. Results clearly show that the MMC method is less sensitive to noise. The MSE in reconstructed blurred data performed by MMC is also plotted vs. SNR. Finally, the reconstructed input signal by MMC and FMC techniques are given for SNR of 30.

Citation Download Citation

Abolfazl M. Amini, Abolfazl M. Amini, } "Performance analysis on Monte Carlo deconvolution decision rules in presence of noise", Proc. SPIE 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems, (25 June 1999); doi: 10.1117/12.351321; https://doi.org/10.1117/12.351321

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