Open access
Author
Date
2021-04Type
- Bachelor Thesis
ETH Bibliography
yes
Altmetrics
Abstract
Cryogenic electron microscopy (cryo-EM) has emerged as the preferred method for imaging macromolecular structures. Like most other high-powered imaging techniques it suffers from a trade-off between acquisition time and quality. Traditional denoising methods for cryo-EM include averaging and Fourier cropping. They are not only resource-intensive but also lose out on high-frequency details. Other denoising methods rely on constructing a model of the noisy process and rely on simplified assumptions. In this study, we train a neural network to model the noisy process and output denoised 3D cryo-EM density maps while minimizing the loss of high-frequency components; even in the absence of ‘clean’ target maps for training. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000480083Publication status
publishedPublisher
ETH ZurichSubject
Cryo-electron microscopy; cryo-EM; deep learning; denoising; Image Processing and Computer Vision; Unsupervised learningOrganisational unit
02891 - ScopeM / ScopeM
02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.
More
Show all metadata
ETH Bibliography
yes
Altmetrics