MULTI-TARGET DETECTION WITH ROTATIONS

Tamir Bendory, Ti Yen Lan, Nicholas F. Marshall*, Iris Rukshin, Amit Singer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We consider the multi-target detection problem of estimating a two-dimensional target image from a large noisy measurement image that contains many randomly rotated and translated copies of the target image. Motivated by single-particle cryo-electron microscopy, we focus on the low signalto-noise regime, where it is difficult to estimate the locations and orientations of the target images in the measurement. Our approach uses autocorrelation analysis to estimate rotationally and translationally invariant features of the target image. We demonstrate that, regardless of the level of noise, our technique can be used to recover the target image when the measurement is sufficiently large.

Original languageEnglish
Pages (from-to)362-380
Number of pages19
JournalInverse Problems and Imaging
Volume17
Issue number2
DOIs
StatePublished - 1 Apr 2023

Funding

FundersFunder number
NSF-BSF2019752
Simons Foundation Math+
Zimin Institute for Engineering Solutions Advancing Better Lives
National Science FoundationDMS-2009753, DMS-1903015, IIS-1837992
National Institutes of Health
National Institute of General Medical Sciences1R01GM136780-01
Air Force Office of Scientific Research
Bloom's Syndrome Foundation2020159
Blanche Moore Foundation
Iowa Science Foundation1924/21

    Keywords

    • Multi-target detection
    • autocorrelation analysis
    • bispectrum
    • cryo-EM
    • singleparticle reconstruction

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