Multi-Target Detection With an Arbitrary Spacing Distribution

被引:1
作者
Lan, Ti-Yen [1 ,2 ]
Bendory, Tamir [1 ,2 ,3 ]
Boumal, Nicolas [1 ,2 ]
Singer, Amit [1 ,2 ]
机构
[1] Princeton Univ, Program Appl & Computat Math, Princeton, NJ 08544 USA
[2] Princeton Univ, Math Dept, Princeton, NJ 08544 USA
[3] Tel Aviv Univ, Sch Elect Engn, Tel Aviv, Israel
关键词
Correlation; Signal to noise ratio; Noise measurement; Noise level; Approximation algorithms; Gaussian noise; Complexity theory; Autocorrelation analysis; expectation maximization; frequency marching; cryo-EM; blind deconvolution; DECONVOLUTION; MICROSCOPY;
D O I
10.1109/TSP.2020.2975943
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Motivated by the structure reconstruction problem in single-particle cryo-electron microscopy, we consider the multitarget detection model, where multiple copies of a target signal occur at unknown locations in a long measurement, further corrupted by additive Gaussian noise. At lownoise 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 approachesautocorrelation 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-3 in the low SNR regime.
引用
收藏
页码:1589 / 1601
页数:13
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