19 February 2013 Poisson shot noise parameter estimation from a single scanning electron microscopy image
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Abstract
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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Stephen Kockentiedt, Klaus Tönnies, Erhardt Gierke, Nico Dziurowitz, Carmen Thim, Sabine Plitzko, "Poisson shot noise parameter estimation from a single scanning electron microscopy image", Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550N (19 February 2013); doi: 10.1117/12.2008374; https://doi.org/10.1117/12.2008374
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