  The inability to acquire clean high-resolution (HR) electron microscopy (EM)
images over a large brain tissue volume hampers many neuroscience studies. To
address this challenge, we propose a deep-learning-based image super-resolution
(SR) approach to computationally reconstruct clean HR 3D-EM with a large field
of view (FoV) from noisy low-resolution (LR) acquisition. Our contributions are
I) Investigating training with no-clean references for $\ell_2$ and $\ell_1$
loss functions; II) Introducing a novel network architecture, named EMSR, for
enhancing the resolution of LR EM images while reducing inherent noise; and,
III) Comparing different training strategies including using acquired LR and HR
image pairs, i.e., real pairs with no-clean references contaminated with real
corruptions, the pairs of synthetic LR and acquired HR, as well as acquired LR
and denoised HR pairs. Experiments with nine brain datasets showed that
training with real pairs can produce high-quality super-resolved results,
demonstrating the feasibility of training with non-clean references for both
loss functions. Additionally, comparable results were observed, both visually
and numerically, when employing denoised and noisy references for training.
Moreover, utilizing the network trained with synthetically generated LR images
from HR counterparts proved effective in yielding satisfactory SR results, even
in certain cases, outperforming training with real pairs. The proposed SR
network was compared quantitatively and qualitatively with several established
SR techniques, showcasing either the superiority or competitiveness of the
proposed method in mitigating noise while recovering fine details.
