GitHub - PrincetonUniversity/cwf_denoise: Covariance Estimation and Denoising for Cryo-EM Images (Covariance Wiener Filtering)

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Covariance Estimation and Denoising for Cryo-EM Images (Covariance Wiener Filtering)

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Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
cwf_functions		cwf_functions
cwf_scripts/simulation		cwf_scripts/simulation
ffb		ffb
kn_rankest		kn_rankest
cwf_paths.m		cwf_paths.m
readme.txt		readme.txt

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This is a MATLAB package for denoising CTF -affected cryo-EM images based on the following manuscripts:
1) Denoising and Covariance Estimation of Single Particle Cryo-EM Images
Tejal Bhamre, Teng Zhang, Amit Singer
http://arxiv.org/abs/1602.06632

2) Fast Steerable Principal Component Analysis
Zhizhen Zhao, Yoel Shkolnisky, Amit Singer
http://arxiv.org/abs/1412.0781

The folder kn_rankest includes code for rank estimation by S. Kritchman and B. Nadler.

DEPENDENCIES
----------------

This package should be used in conjunction with the cryo-EM tool box ASPIRE (http://spr.math.princeton.edu/) and will be included in the latest version of ASPIRE. It also requires the NUFFT package (to be included in the latest version of ASPIRE) available
at http://www.cims.nyu.edu/cmcl/nufft/nufft.html.

INSTRUCTIONS
----------------

1) Download and install ASPIRE from http://spr.math.princeton.edu/ following the instructions for installation.
2) Add ASPIRE files in your MATLAB path using initpath.m in ASPIRE.
3) If this package is in a separate location than ASPIRE, add the package to your MATLAB path using cwf_paths.m  
4) Enjoy the example simulation scripts in cwf_scripts to denoise images.

In case of issues or questions, please email Tejal ([email protected]) and Jane ([email protected]).