Improved Poisson intensity estimation: denoising application using Poisson data.

H Lu; Y Kim; John M M Anderson

doi:10.1109/tip.2003.822606

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Improved Poisson intensity estimation: denoising application using Poisson data.

H Lu·Y Kim·John M M Anderson

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Abstract

Recently, Timmermann and Nowak developed algorithms for estimating the means of independent Poisson random variables. The algorithms are based on a multiscale model where certain random variables are assumed to obey a beta-mixture density function. Timmermann and Nowak simplify the density estimation problem by assuming the beta parameters are known and only one mixture parameter is unknown. They use the observed data and the method of moments to estimate the unknown mixture parameter. Taking a different approach, we generate training data from the observed data and compute maximum likelihood estimates of all of the beta-mixture parameters. To assess the improved performance obtained by the proposed modification, we consider a denoising application using Poisson data.

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Algorithms Image Enhancement Image Interpretation, Computer-Assisted Likelihood Functions Models, Statistical Poisson Distribution Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Stochastic Processes Comparative Study Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.Validation Study

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H Lu, Y Kim, John M M Anderson. 2004. Improved Poisson intensity estimation: denoising application using Poisson data.. https://doi.org/10.1109/tip.2003.822606

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