Beam Cross Sections Create Mixtures: Improving Feature Localization in Secondary Electron Imaging
This paper demonstrates that modeling secondary electron counts as a mixture distribution, rather than a simple convolution, enables a maximum likelihood estimator that achieves significant sub-pixel edge localization accuracy—reducing root mean-squared error by approximately five-fold compared to conventional methods in both simulations and real helium ion microscopy datasets.