Background and Objective: The contrast of cryo-EM images varies from one to another, primarily due to the uneven thickness of the ice layer. This contrast variation 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 at the earlier computational stages of class averaging and ab-initio modeling. This paper aims to solve the contrast estimation problem directly from the picked particle images in the ab-initio stage, without estimating the 3-D volume, image rotations, or class averages. 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 we apply 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 restoration 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.
翻译:背景和目标:低温-EM图像的对比因冰层厚度不均而各异,主要由于冰层厚度不均。对比差异会影响2D类平均、3Dab-initio模型模型和3D异差分析的质量。对比估计目前是在3D迭接改进过程中进行的。因此,在类平均和 ab-initio 模型的早期计算阶段无法提供估计数。本文件旨在直接解决从AB-initio 阶段选取的颗粒图像中选取的对比变差估计问题,而没有估计3D类、3D类、3Dab-initio 模型和3D类平均数。方法:我们分析的主要观察是原始图像的 2D 共变差矩阵与基本清洁图像的相异性有关,噪音差异和图像之间的对比变异性。我们表明,对比差异变异性可来自2D变异性矩阵矩阵,我们应用现有的变异性 Wiener 过滤(CWFFF) 框架来估算它。我们还演示了3级图像的更精确性估算,我们比较了CFFFFS 方法, 的比更能方法的比更能、更能、更能、更精确分析。