Poisson shot noise parameter estimation from a single scanning electron microscopy image

Stephen Kockentiedt; Klaus Tönnies; Erhardt Gierke; Nico Dziurowitz; Carmen Thim; Sabine Plitzko

doi:10.1117/12.2008374

19 February 2013 Poisson shot noise parameter estimation from a single scanning electron microscopy image

Stephen Kockentiedt, Klaus Tönnies, Erhardt Gierke, Nico Dziurowitz, Carmen Thim, Sabine Plitzko

Proceedings Volume 8655, Image Processing: Algorithms and Systems XI; 86550N (2013) https://doi.org/10.1117/12.2008374
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

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.

Citation Download Citation

Stephen Kockentiedt, Klaus Tönnies, Erhardt Gierke, Nico Dziurowitz, Carmen Thim, and 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); https://doi.org/10.1117/12.2008374

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