Scanning electron microscopy (SEM) has an extremely low signal-to-noise ratio leading to a high level of shot noise which makes further processing difficult. Unlike often assumed, the noise stems from a Poisson process and is not Gaussian but depends on the signal level.
A method to estimate the noise parameters of individual images should be found.
Using statistical modeling of SEM noise, a robust optimal noise estimation algorithm is derived.
A non-local means noise reduction filter tuned with the estimated noise parameters on average achieves an 18% lower root-mean-square error than the untuned filter on simulated images.
The algorithm is stable and can adapt to varying noise levels.
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