WikiSysop: Created page with "== Citation == Ma, C., Bendory, T., Boumal, N., Sigworth, F. and Singer, A. 2020. Heterogeneous multireference alignment for images with application to 2D classification in single particle reconstruction. IEEE Transactions on Image Processing. 29, (2020), 1699–1710. == Abstract == Motivated by the task of 2D classification in single particle reconstruction by cryo-electron microscopy (cryo-EM), we consider the problem of heterogeneous multireference alignment of ima..."

2025-09-25T06:14:14Z

Created page with "== Citation == Ma, C., Bendory, T., Boumal, N., Sigworth, F. and Singer, A. 2020. Heterogeneous multireference alignment for images with application to 2D classification in single particle reconstruction. IEEE Transactions on Image Processing. 29, (2020), 1699–1710. == Abstract == Motivated by the task of 2D classification in single particle reconstruction by cryo-electron microscopy (cryo-EM), we consider the problem of heterogeneous multireference alignment of ima..."

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== Citation ==

Ma, C., Bendory, T., Boumal, N., Sigworth, F. and Singer, A. 2020. Heterogeneous multireference alignment for images with application to 2D classification in single particle reconstruction. IEEE Transactions on Image Processing. 29, (2020), 1699–1710.

== Abstract ==

Motivated by the task of 2D classification in single
particle reconstruction by cryo-electron microscopy (cryo-EM),
we consider the problem of heterogeneous multireference alignment
of images. In this problem, the goal is to estimate a (typically
small) set of target images from a (typically large) collection
of observations. Each observation is a rotated, noisy version
of one of the target images. For each individual observation,
neither the rotation nor which target image has been rotated are
known. As the noise level in cryo-EM data is high, clustering the
observations and estimating individual rotations is challenging.
We propose a framework to estimate the target images directly
from the observations, completely bypassing the need to cluster
or register the images. The framework consists of two steps.
First, we estimate rotation-invariant features of the images, such
as the bispectrum. These features can be estimated to any
desired accuracy, at any noise level, provided sufficiently many
observations are collected. Then, we estimate the images from the
invariant features. Numerical experiments on synthetic cryo-EM
datasets demonstrate the effectiveness of the method. Ultimately,
we outline future developments required to apply this method to
experimental data.

== Keywords ==

== Links ==

https://ieeexplore.ieee.org/abstract/document/8864095/

== Related software ==

== Related methods ==

== Comments ==

2020Ma RotationInvariant - Revision history