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Background and Objective: The contrast of cryo-EM images varies from one toanother, primarily due to the uneven thickness of the ice layer. This contrastvariation can affect the quality of 2-D class averaging, 3-D ab-initiomodeling, and 3-D heterogeneity analysis. Contrast estimation is currentlyperformed during 3-D iterative refinement. As a result, the estimates are notavailable at the earlier computational stages of class averaging and ab-initiomodeling. This paper aims to solve the contrast estimation problem directlyfrom the picked particle images in the ab-initio stage, without estimating the3-D volume, image rotations, or class averages.  Methods: The key observation underlying our analysis is that the 2-Dcovariance matrix of the raw images is related to the covariance of theunderlying clean images, the noise variance, and the contrast variabilitybetween images. We show that the contrast variability can be derived from the2-D covariance matrix and we apply the existing Covariance Wiener Filtering(CWF) framework to estimate it. We also demonstrate a modification of CWF toestimate 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 oftencomparable to that of an oracle that knows the ground truth covariance of theclean images. The more accurate contrast estimation also improves the qualityof image restoration as demonstrated in both synthetic and experimentaldatasets.  Conclusions: This paper proposes an effective method for contrast estimationdirectly from noisy images without using any 3-D volume information. It enablescontrast correction in the earlier stage of single particle analysis, and mayimprove the accuracy of downstream processing.

Ab-initio Contrast Estimation and Denoising of Cryo-EM Images