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bioRxiv · 10.1101/2020.08.16.253070

Enhancing SNR and generating contrast for cryo-EM images with convolutional neural networks

Palovcak, E.·Asarnow, D.·Campbell, M. G.·Yu, Z.·Cheng, Y.
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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 high-frequency SNR, which is suppressed by high-defocus imaging and removed by low pass filtration. Here, we demonstrate that a convolutional neural network (CNN) denoising algorithm can be used to significantly enhance SNR and generate contrast in cryo-EM images. We provide a quantitative evaluation of bias introduced by the denoising procedure and its influences on image processing and three-dimensional reconstructions. Our study suggests that besides enhancing the visual contrast of cryo-EM images, the enhanced SNR of denoised images may facilitate better outcomes in the other parts of the image processing pipeline, such as classification and 3D alignment. Overall, our results provide a ground of using denoising CNNs in the cryo-EM image processing pipeline.

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Palovcak, E., Asarnow, D., Campbell, M. G., Yu, Z., Cheng, Y.. 2020-08-16. Enhancing SNR and generating contrast for cryo-EM images with convolutional neural networks. https://doi.org/10.1101/2020.08.16.253070

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