Abstract
Frieden and Wells have derived a maximum likelihood (ML) image restoration algorithm that accurately models the noise in each image element using Poisson counting statistics. This method also leads naturally to “maximum entropy” like ideas. They have reported a rather dramatic resolution enhancement when this method was applied to low light level astronomical images in which the image noise was essentially due to photon counting statistics. This low light level situation also accurately models the ADF (annular dark field) STEM (scanning transmission microscope) image if the image is aquired digitally by electronically counting the individual scattered electrons for each position of the focused probe.The simplest ADF-STEM image model assumes that the electron intensity distribution in the focused probe is the incoherent point spread function (psf) of the image.where g(x)=recorded image, f(x)=ideal image, n(x)=random noise, * represents convolution and
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