2020Huang SuperResolution
Citation
Huang, Q.; Zhou, Y.; Du, X.; Chen, R.; Wang, J.; Rudin, C.; Bartesaghi, A. Cryo-ZSSR: multiple-image super-resolution basedon deep internal learning. Proc. 34th Conf. on Neural Information Processing Systems, 2020
Abstract
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.
Keywords
Links
https://www.mlsb.io/papers/MLSB2020_Cryo-ZSSR:_multiple-image_super-resolution_based.pdf
