Thursday, November 19, 1998
4:30 - 5:30 PM
1311 EECS
Abstract-
Many important problems in engineering and science are well
modeled by Poisson processes. In many applications it is of
great interest to accurately estimate the intensities
underlying observed Poisson data. This talk discusses a new
Bayesian approach to Poisson intensity estimation based on
the Haar wavelet transform. It is shown that the Haar
transform provides a very natural and powerful framework for
this problem. Using this framework, a novel multiscale
Bayesian prior probability model for intensity functions is
devised. The new prior leads to a simple, Bayesian
intensity estimation procedure. Furthermore, the correlation
function of the new prior is shown to have 1/f spectral
characteristics, making it especially well suited to image
modeling. Applications of the new framework to
photon-limited image estimation, and nuclear medicine
imaging in particular, are discussed.
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