Two-Dimensional Multi-Target Detection: An Autocorrelation Analysis Approach

Shay Kreymer*, Tamir Bendory

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We consider thetwo-dimensional multi-target detection problem of recovering a target image from a noisy measurement that contains multiple copies of the image, each randomly rotated and translated. Motivated by the structure reconstruction problem in single-particle cryo-electron microscopy, we focus on the high noise regime, where the noise hampers accurate detection of the image occurrences. We develop an autocorrelation analysis framework to estimate the image directly from a measurement with an arbitrary spacing distribution of image occurrences, bypassing the estimation of individual locations and rotations. We conduct extensive numerical experiments, and demonstrate image recovery in highly noisy environments.

Original languageEnglish
Pages (from-to)835-849
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume70
DOIs
StatePublished - 2022

Keywords

  • Autocorrelation analysis
  • cryo-electron microscopy
  • multi-target detection

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