Wavelet-based Poisson rate estimation using the Skellam distribution

Owing to the stochastic nature of discrete processes such as photon counts in imaging, real-world data measurements often exhibit heteroscedastic behavior. In particular, time series components and other measurements may frequently be assumed to be non-iid Poisson random variables, whose rate parameter is proportional to the underlying signal of interest--witness literature in digital communications, signal processing, astronomy, and magnetic resonance imaging applications. In this work, we show that certain wavelet and filterbank transform coefficients corresponding to vector-valued measurements of this type are distributed as sums and differences of independent Poisson counts, taking the so-called Skellam distribution. While exact estimates rarely admit analytical forms, we present Skellam mean estimators under both frequentist and Bayes models, as well as computationally efficient approximations and shrinkage rules, that may be interpreted as Poisson rate estimation method performe...

• Type: Conference
• Year: 2009
• Where: CIMAGING
• Authors: Keigo Hirakawa, Farhan A. Baqai, Patrick J. Wolfe
• Keywords: CIMAGING 2008, CIMAGING 2009, Poisson, Poisson Counts, Poisson Random Variables