Two-Dimensional Multi-Target Detection: An Autocorrelation Analysis Approach
Two-Dimensional Multi-Target Detection: An Autocorrelation Analysis Approach
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DOI：
10.1109/tsp.2022.3147735
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发表时间：
2022-01-01
影响因子：
5.4
通讯作者：
Bendory, Tamir
中科院分区：
文献类型：
--
作者：
Kreymer, Shay;Bendory, Tamir
我们考虑二维多目标检测问题，从噪声测量中恢复目标图像，其中包含多个图像副本，每个副本随机旋转和平移。在单粒子冷冻电子显微镜的结构重建问题的动机，我们专注于高噪声制度，噪声阻碍准确检测的图像发生。我们开发了一个自相关分析框架，直接从图像发生的任意间距分布的测量中估计图像，绕过了对单个位置和旋转的估计。我们进行了广泛的数值实验，并证明在高噪声环境中的图像恢复。（一）
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.(1)