  Perhaps surprisingly, the total electron microscopy (EM) data collected to
date is less than a cubic millimeter. Consequently, there is an enormous demand
in the materials and biological sciences to image at greater speed and lower
dosage, while maintaining resolution. Traditional EM imaging based on
homogeneous raster-order scanning severely limits the volume of high-resolution
data that can be collected, and presents a fundamental limitation to
understanding physical processes such as material deformation, crack
propagation, and pyrolysis.
  We introduce a novel multi-resolution data fusion (MDF) method for
super-resolution computational EM. Our method combines innovative data
acquisition with novel algorithmic techniques to dramatically improve the
resolution/volume/speed trade-off. The key to our approach is to collect the
entire sample at low resolution, while simultaneously collecting a small
fraction of data at high resolution. The high-resolution measurements are then
used to create a material-specific patch-library that is used within the
"plug-and-play" framework to dramatically improve super-resolution of the
low-resolution data. We present results using FEI electron microscope data that
demonstrate super-resolution factors of 4x, 8x, and 16x, while substantially
maintaining high image quality and reducing dosage.
