  To date, live-cell imaging at the nanometer scale remains challenging. Even
though super-resolution microscopy methods have enabled visualization of
subcellular structures below the optical resolution limit, the spatial
resolution is still far from enough for the structural reconstruction of
biomolecules in vivo (i.e. ~24 nm thickness of microtubule fiber). In this
study, we proposed an A-net network and showed that the resolution of
cytoskeleton images captured by a confocal microscope can be significantly
improved by combining the A-net deep learning network with the DWDC algorithm
based on degradation model. Utilizing the DWDC algorithm to construct new
datasets and taking advantage of A-net neural network's features (i.e.,
considerably fewer layers), we successfully removed the noise and flocculent
structures, which originally interfere with the cellular structure in the raw
image, and improved the spatial resolution by 10 times using relatively small
dataset. We, therefore, conclude that the proposed algorithm that combines
A-net neural network with the DWDC method is a suitable and universal approach
for exacting structural details of biomolecules, cells and organs from
low-resolution images.
