  The most sophisticated existing methods to generate 3D isotropic
super-resolution (SR) from non-isotropic electron microscopy (EM) are based on
learned dictionaries. Unfortunately, none of the existing methods generate
practically satisfying results. For 2D natural images, recently developed
super-resolution methods that use deep learning have been shown to
significantly outperform the previous state of the art.
  We have adapted one of the most successful architectures (FSRCNN) for 3D
super-resolution, and compared its performance to a 3D U-Net architecture that
has not been used previously to generate super-resolution.
  We trained both architectures on artificially downscaled isotropic ground
truth from focused ion beam milling scanning EM (FIB-SEM) and tested the
performance for various hyperparameter settings.
  Our results indicate that both architectures can successfully generate 3D
isotropic super-resolution from non-isotropic EM, with the U-Net performing
consistently better. We propose several promising directions for practical
application.
