Compound and filtered Poison processes are useful models for many applications in signal processing, image processing, an dudcommunications . One of the earliest imaging applications of these models was proposed by Bernard Picinbono in a 1955 paperudon silver dye photographs . In this paper we treat a generalized model with the primiary objective being to estimate parameter sudof the filtered Poisson process in the presence of spatial smoothing and additive Gaussian noise . By imbedding the estimatio nudproblem into the context of information theory we decompose the model into the cascade of a discrete event Poisson proces sudchannel and a continuous Gaussian waveform channel . This naturally leads to a expectation-maximization (EM) type estimatio nudalgorithm and a distortion-rate lower bound on estimation error .
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