Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks.

ABSTRACT: In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration. Recently, convolutional neural networks (CNNs) trained to denoise cryo-EM images have produced impressive gains in image contrast, but it is not clear how these algorithms affect the information content of the image. Here, a denoising CNN for cryo-EM images was

SUBMITTER: Palovcak E 

PROVIDER: S-EPMC7642784 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature