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Generative Adversarial Networks for Robust Cryo-EM Image Denoising
Hanlin Gu
,
Poline XIAN
, Ilona Christy Unarta
, Yuan Yao
*
*
Corresponding author for this work
School of Continuing Education
Research output
:
Chapter in book/report/conference proceeding
›
Chapter
›
peer-review
2
Citations (Scopus)
Overview
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Dive into the research topics of 'Generative Adversarial Networks for Robust Cryo-EM Image Denoising'. Together they form a unique fingerprint.
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Keyphrases
Image Denoising
100%
Generative Adversarial Networks
100%
Cryo-EM Images
100%
Cryogenic Electron Microscopy
66%
Robust Denoising
50%
Denoising
33%
2D Image
33%
Traditional Image
33%
Denoising Autoencoder
33%
3D Reconstruction
33%
Proposed Methodology
16%
Popular
16%
Signal-to-noise Ratio
16%
Mean Square Error
16%
Effective Tool
16%
Plasmodium Falciparum
16%
Robust Estimation
16%
Contamination Distribution
16%
Low Signal-to-noise Ratio
16%
Simulated Dataset
16%
Denoising Problems
16%
RNA-dependent RNA Polymerase (RdRP)
16%
Structure Determination
16%
Peak Signal to Noise Ratio
16%
Denoising Techniques
16%
Electron Microscopy Images
16%
Art Performance
16%
Autoencoder
16%
High-frequency Noise
16%
Structural Similarity Index
16%
Ratio Data
16%
Macromolecular Structure
16%
80S Ribosome
16%
Huber Contamination
16%
Contamination Model
16%
Statistical Optimality
16%
Image Generative Model
16%
3D Structure Reconstruction
16%
Thermus Aquaticus
16%
Computer Science
image denoising
100%
Generative Adversarial Networks
100%
de-noising
83%
3d Reconstruction
33%
Denoising Autoencoder
33%
Signal-to-Noise Ratio
33%
Captured Image
16%
Denoising Problem
16%
peak signal to noise ratio
16%
Generative Model
16%
Structural Similarity
16%
Art Performance
16%
Autoencoder
16%
Quantitative Comparison
16%
Engineering
Signal-to-Noise Ratio
100%
Denoising Autoencoders
66%
Optimality
33%
Denoising Problem
33%
Captured Image
33%
Peak Signal
33%
Autoencoder
33%
Mean Square Error
33%
Structural Similarity
33%
Generative Model
33%
Similarity Index
33%
Mathematics
Noise Ratio
100%
Autoencoder
100%
3D-Reconstruction
66%
Mean Square Error
33%
Optimality
33%
Robust Estimation
33%
Outlier
33%
Material Science
Cryo-Electron Microscopy
100%
Signal-to-Noise Ratio
60%
RNA Polymerase
20%
Structure Determination
20%