Andy Zhang, Oscar Mickelin, Amit Singer · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.22840 · cited by 0 works (OpenAlex) · open-access PDF
Single-particle cryogenic electron microscopy (cryo-EM) is an imaging technique capable of reconstructing high-resolution three-dimensional (3D) structure of biological macromolecules from a set of noisy projection images taken at random viewing directions. The mathematical model for cryo-EM images typically assumes that each image contains exactly one particle, which is centered at the origin. However, many experimental datasets contain particle images which include multiple particles: one central particle and a few crowding particles. This deviation from the model leads to inaccurate estimation of image statistics, such as the low-order image moments. To account for this, we propose a method to estimate image moments while correcting for the effect of crowding particles. This method can be run directly from a set of cryo-EM micrographs with picked particles, and we demonstrate its efficacy on a wide array of synthetic data.
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Topics: Image (mathematics), Artificial intelligence, Moment (physics), Set (abstract data type), Computer vision, Projection (relational algebra)
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