The problem of estimating the parameters of a Poisson-Gaussian model from experimental data has recently raised much interest in various applications, especially for CCD imaging systems. In this context, a field of independent random variables is observed, which is varying both in time and space. Each variable is a sum of two components, one following a Poisson and the other a Gaussian distribution. In this paper, a general formulation is considered where the associated Poisson process is nonstationary in space and also exhibits an exponential decay in time, whereas the Gaussian component corresponds to a stationary white noise with arbitrary mean. To solve the considered parametric estimation problem, an iterative Expectation-Maximization (EM) approach is proposed. Much attention is paid to the initialization of the EM algorithm for which an adequate moment-based method using recent optimization tools is proposed. In addition, a performance analysis of the proposed approach is carried out by computing the Cramer-Rao bounds on the estimated variables. The performance of the proposed estimation procedure is illustrated on both synthetic data and real fluorescence microscopy image sequences. The algorithm is shown to provide reliable estimates of the mean/variance of the Gaussian noise and of the scale parameter of the Poisson component, as well as of its exponential decay rate.