Ab-initio Contrast Estimation and Denoising of Cryo-EM Images
Yunpeng Shi, Amit Singer
Background and Objective: The contrast of cryo-EM images vary from one to
another, primarily due to the uneven thickness of ice layers. The variation of
contrast can affect the quality of 2-D class averaging, 3-D ab-initio modeling,
and 3-D heterogeneity analysis. Contrast estimation is currently performed
during 3-D iterative refinement. As a result, the estimates are not available
for class averaging and ab-initio modeling. However, these methods require good
initial estimates of 3-D volumes and 3-D rotations of molecules. This paper
aims to solve the contrast estimation problem in the ab-initio stage, without
estimating the 3-D volume.
Methods: The key observation underlying our analysis is that the 2-D
covariance matrix of the raw images is related to the covariance of the
underlying clean images, the noise variance, and the contrast variability
between images. We show that the contrast variability can be derived from the
2-D covariance matrix and use the existing Covariance Wiener Filtering (CWF)
framework to estimate it. We also demonstrate a modification of CWF to estimate
the contrast of individual images.
Results: Our method improves the contrast estimation by a large margin,
compared to the previous CWF method. Its estimation accuracy is often
comparable to that of an oracle that knows the ground truth covariance of the
clean images. The more accurate contrast estimation also improves the quality
of image denoising as demonstrated in both synthetic and experimental datasets.
Conclusions: This paper proposes an effective method for contrast estimation
directly from noisy images without using any 3-D volume information. It enables
contrast correction in the earlier stage of single particle analysis, and may
improve the accuracy of downstream processing.