  Single-particle cryo-electron microscopy (cryo-EM) is an emerging imaging
modality capable of visualizing proteins and macro-molecular complexes at
near-atomic resolution. The low electron-doses used to prevent sample radiation
damage, result in images where the power of the noise is 100 times greater than
the power of the signal. To overcome the low-SNRs, hundreds of thousands of
particle projections acquired over several days of data collection are averaged
in 3D to determine the structure of interest. Meanwhile, recent image
super-resolution (SR) techniques based on neural networks have shown state of
the art performance on natural images. Building on these advances, we present a
multiple-image SR algorithm based on deep internal learning designed
specifically to work under low-SNR conditions. Our approach leverages the
internal image statistics of cryo-EM movies and does not require training on
ground-truth data. When applied to a single-particle dataset of apoferritin, we
show that the resolution of 3D structures obtained from SR micrographs can
surpass the limits imposed by the imaging system. Our results indicate that the
combination of low magnification imaging with image SR has the potential to
accelerate cryo-EM data collection without sacrificing resolution.
