Centering noisy images with application to cryo-EM

TL;DR

This paper introduces a robust method for estimating the center of noisy 2-D images, particularly improving alignment in cryo-EM, by using a surrogate function to the geometric median that outperforms standard approaches.

Contribution

The paper proposes a novel surrogate function for the geometric median, enhancing center estimation in noisy images and improving cryo-EM 3D reconstruction accuracy.

Findings

The surrogate function successfully estimates the center of mass in highly noisy images.

Application to cryo-EM improves molecular alignment and reconstruction quality.

Method reduces computational time and supports higher resolution in cryo-EM.

Abstract

We target the problem of estimating the center of mass of noisy 2-D images. We assume that the noise dominates the image, and thus many standard approaches are vulnerable to estimation errors. Our approach uses a surrogate function to the geometric median, which is a robust estimator of the center of mass. We mathematically analyze cases in which the geometric median fails to provide a reasonable estimate of the center of mass, and prove that our surrogate function leads to a successful estimate. One particular application for our method is to improve 3-D reconstruction in single-particle cryo-electron microscopy (cryo-EM). We show how to apply our approach for a better translational alignment of macromolecules picked from experimental data. In this way, we facilitate the succeeding steps of reconstruction and streamline the entire cryo-EM pipeline, saving valuable computational time and supporting resolution enhancement.