MULTI-TARGET DETECTION WITH ROTATIONS

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

doi:10.3934/ipi.2022046

MULTI-TARGET DETECTION WITH ROTATIONS

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

^*Corresponding author for this work

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	362-380
Number of pages	19
Journal	Inverse Problems and Imaging
Volume	17
Issue number	2
DOIs	https://doi.org/10.3934/ipi.2022046
State	Published - 1 Apr 2023

Funding

Funders	Funder number
NSF-BSF	2019752
Simons Foundation Math+
Zimin Institute for Engineering Solutions Advancing Better Lives
National Science Foundation	DMS-2009753, DMS-1903015, IIS-1837992
National Institutes of Health
National Institute of General Medical Sciences	1R01GM136780-01
Air Force Office of Scientific Research
Bloom's Syndrome Foundation	2020159
Blanche Moore Foundation
Iowa Science Foundation	1924/21

Keywords

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

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10.3934/ipi.2022046

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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.",

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author = "Tamir Bendory and Lan, {Ti Yen} and Marshall, {Nicholas F.} and Iris Rukshin and Amit Singer",

year = "2023",

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doi = "10.3934/ipi.2022046",

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AU - Singer, Amit

PY - 2023/4/1

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N2 - 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.

AB - 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.

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KW - autocorrelation analysis

KW - bispectrum

KW - cryo-EM

KW - singleparticle reconstruction

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MULTI-TARGET DETECTION WITH ROTATIONS

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