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Journal of Structural Biology
Article . 2016 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2016
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
DBLP
Article . 2024
Data sources: DBLP
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Denoising and covariance estimation of single particle cryo-EM images

Authors: Bhamre, Tejal; Zhang, Teng; Singer, Amit;

Denoising and covariance estimation of single particle cryo-EM images

Abstract

The problem of image restoration in cryo-EM entails correcting for the effects of the Contrast Transfer Function (CTF) and noise. Popular methods for image restoration include `phase flipping', which corrects only for the Fourier phases but not amplitudes, and Wiener filtering, which requires the spectral signal to noise ratio. We propose a new image restoration method which we call `Covariance Wiener Filtering' (CWF). In CWF, the covariance matrix of the projection images is used within the classical Wiener filtering framework for solving the image restoration deconvolution problem. Our estimation procedure for the covariance matrix is new and successfully corrects for the CTF. We demonstrate the efficacy of CWF by applying it to restore both simulated and experimental cryo-EM images. Results with experimental datasets demonstrate that CWF provides a good way to evaluate the particle images and to see what the dataset contains even without 2D classification and averaging.

Revision for JSB

Country
United States
Keywords

FOS: Computer and information sciences, Macromolecular Substances, Emetine, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, TRPV Cation Channels, Machine Learning (stat.ML), Signal-To-Noise Ratio, Statistics - Machine Learning, CTF correction, Image Processing, Computer-Assisted, Animals, Inositol 1,4,5-Trisphosphate Receptors, Computer Simulation, Particle Size, Wiener filtering, Bacteria, Fourier Analysis, Cryoelectron Microscopy, Biomolecules (q-bio.BM), Quantitative Biology - Biomolecules, Steerable PCA, FOS: Biological sciences, Ribosomes, Algorithms

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
56
Top 10%
Top 10%
Top 10%
Green
bronze