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16 August 2001 Metropolis Monte Carlo deconvolution technique compared to iterative methods for noisy data
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Abstract
The Metropolis Monte Carlo (MMC) technique performance is compared to the performance of an iterative deconvolution method. In the MMC approach to deconvolution two Monte Carlo Procedure (MCP) are run at the same time. In one the blurred data is used as a distribution function for selection of pixels. And the second MCP decides whether to place a grain in the true data (true input) or not. We show that this approach improves the reconstruction process. The blurred data is obtained by convolving a 24 points input signal that has three peaks with a 21 points wide Guassian impulse response function (IRF). 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 Signal-to-Noise Ratios (SNR) studied range from 10 to 150. The type of noise used in this study is Gaussian Distributed additive noise.
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Abolfazl M. Amini, George E. Ioup, and Juliette W. Ioup "Metropolis Monte Carlo deconvolution technique compared to iterative methods for noisy data", Proc. SPIE 4380, Signal Processing, Sensor Fusion, and Target Recognition X, (16 August 2001); https://doi.org/10.1117/12.436983
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