  This paper is concerned with investigating super-resolution algorithms and
solutions for handling electron microscopic images. We note two main aspects
differentiating the problem discussed here from those considered in the
literature. The first difference is that in the electron imaging setting. We
have a pair of physical high-resolution and low-resolution images, rather than
a physical image with its downsampled counterpart. The high-resolution image
covers about 25% of the view field of the low-resolution image, and the
objective is to enhance the area of the low-resolution image where there is no
high-resolution counterpart. The second difference is that the physics behind
electron imaging is different from that of optical (visible light) photos. The
implication is that super-resolution models trained by optical photos are not
effective when applied to electron images. Focusing on the unique properties,
we devise a global and local registration method to match the high- and
low-resolution image patches and explore training strategies for applying deep
learning super-resolution methods to the paired electron images. We also
present a simple, non-local-mean approach as an alternative. This alternative
performs as a close runner-up to the deep learning approaches, but it takes
less time to train and entertains a simpler model structure.
