Estimation in Poisson Noise: Properties of the Conditional Mean Estimator
Alex Dytso, H. Vincent Poor

TL;DR
This paper analyzes the properties of the conditional mean estimator in Poisson noise, revealing identities, regularity conditions, and the impact of parameters like dark current and scaling coefficient on estimator behavior.
Contribution
It introduces new identities, regularity conditions, and conditions for linearity of the conditional mean estimator in Poisson noise models, considering parameters like dark current and scaling.
Findings
Gradient of estimator related to conditional variance.
Conditional mean uniquely determines input distribution.
Estimator cannot be linear with non-zero dark current.
Abstract
This paper considers estimation of a random variable in Poisson noise with signal scaling coefficient and dark current as explicit parameters of the noise model. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the scaling coefficient, the dark current parameter, the distribution of the input random variable and channel realizations. With respect to the scaling coefficient and the dark current, several identities in terms of derivatives are established. For example, it is shown that the gradient of the conditional mean estimator with respect to the scaling coefficient and dark current parameter is proportional to the conditional variance. Moreover, a score function is proposed and a Tweedie-like formula for the conditional expectation is recovered. With respect to the distribution, several regularity conditions are shown. For instance, it…
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