Expectation-maximization algorithm for amplitude estimation of saturated optical transient signals

Expectation Maximization Deconvolution Algorithm

In this paper, we use a general mathematical and experimental methodology to analyze image deconvolution. The main procedure is to use an example image convolving it with a know Gaussian point spread function and then develop algorithms to recover the image. Observe the deconvolution process by adding Gaussian and Poisson noise at different signal to noise ratios. In addition, we will describe ...

The Expectation Maximization Algorithm

This note represents my attempt at explaining the EM algorithm (Hartley, 1958; Dempster et al., 1977; McLachlan and Krishnan, 1997). This is just a slight variation on TomMinka’s tutorial (Minka, 1998), perhaps a little easier (or perhaps not). It includes a graphical example to provide some intuition. 1 Intuitive Explanation of EM EM is an iterative optimizationmethod to estimate some unknown ...

Expectation-Maximization (EM) algorithm

Convolutional Expectation Maximization for Population Estimation

There is a fundamental spatial mismatch in the data available for estimate population from satellite imagery. Spectral reflectances are available for each pixel of an image, but ground reference population data are available only for larger zones, therefore satellite imagery has bigger resolution than ground reference images. The general response has been to build models for the average populat...

The Noisy Expectation-Maximization Algorithm

We present a noise-injected version of the Expectation-Maximization (EM) algorithm: the Noisy Expectation Maximization (NEM) algorithm. The NEM algorithm uses noise to speed up the convergence of the EM algorithm. The NEM theorem shows that additive noise speeds up the average convergence of the EM algorithm to a local maximum of the likelihood surface if a positivity condition holds. Corollary...