Quantifying Feature Uncertainty in Sub-sampled Low-dose (S)TEM Images

Scanning transmission electron microscopes (STEM) provide high resolution images at an atomic scale. Unfortunately, the level of electron dose required to achieve these high resolution images results in a potentially large amount of specimen damage. A promising approach to mitigate specimen damage is to subsample the specimen [1, 2, 3]. With random sampling, the microscope creates high resolution images of segments of the specimen while reducing overall damage. However, subsampling produces images that have several missing values and can be hard to interpret and analyze in their raw state.