  Scanning Transmission Electron Microscopy (STEM) offers high-resolution
images that are used to quantify the nanoscale atomic structure and composition
of materials and biological specimens. In many cases, however, the resolution
is limited by the electron beam damage, since in traditional STEM, a focused
electron beam scans every location of the sample in a raster fashion. In this
paper, we propose a scanning method based on the theory of Compressive Sensing
(CS) and subsampling the electron probe locations using a line hop sampling
scheme that significantly reduces the electron beam damage. We experimentally
validate the feasibility of the proposed method by acquiring real CS-STEM data,
and recovering images using a Bayesian dictionary learning approach. We support
the proposed method by applying a series of masks to fully-sampled STEM data to
simulate the expectation of real CS-STEM. Finally, we perform the real data
experimental series using a constrained-dose budget to limit the impact of
electron dose upon the results, by ensuring that the total electron count
remains constant for each image.
