ArticleIEEE transactions on signal processing : a publication of the IEEE Signal Processing Society2020
Multi-target Detection with an Arbitrary Spacing Distribution.
Article in IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- EXPECTATION-MAXIMIZATION FOR STRUCTURE DETERMINATION DIRECTLY FROM CRYO-EM MICROGRAPHS.Inverse problems and imaging (Springfield, Mo.) · 2027Article
- Autocorrelation analysis for cryo-EM with sparsity constraints: Improved sample complexity and projection-based algorithms.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- MULTI-TARGET DETECTION WITH ROTATIONS.Inverse problems and imaging (Springfield, Mo.) · 2023Article
- Toward Single Particle Reconstruction without Particle Picking: Breaking the Detection Limit.SIAM journal on imaging sciences · 2023Article
- Random conical tilt reconstruction without particle picking in cryo-electron microscopy.Acta crystallographica. Section A, Foundations and advances · 2022Article
- An approximate expectation-maximization for two-dimensional multi-target detection.IEEE signal processing letters · 2022Article
- Multi-target detection with application to cryo-electron microscopy.Inverse problems · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
Motivated by the structure reconstruction problem in single-particle cryo-electron microscopy, we consider the multi-target detection model, where multiple copies of a target signal occur at unknown locations in a long measurement, further corrupted by additive Gaussian noise. At low noise levels, one can easily detect the signal occurrences and estimate the signal by averaging. However, in the presence of high noise, which is the focus of this paper, detection is impossible. Here, we propose two approaches-autocorrelation analysis and an approximate expectation maximization algorithm-to reconstruct the signal without the need to detect signal occurrences in the measurement. In particular, our methods apply to an arbitrary spacing distribution of signal occurrences. We demonstrate reconstructions with synthetic data and empirically show that the sample complexity of both methods scales as SNR
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