Kreymer Shay, Singer Amit, Bendory Tamir
School of Electrical Engineering of Tel Aviv University, Tel Aviv, Israel.
Department of Mathematics and PACM, Princeton University, Princeton, NJ, USA.
IEEE Signal Process Lett. 2022;29:1087-1091. doi: 10.1109/lsp.2022.3167335. Epub 2022 Apr 13.
We consider the two-dimensional multi-target detection (MTD) problem of estimating a target image from a noisy measurement that contains multiple copies of the image, each randomly rotated and translated. The MTD model serves as a mathematical abstraction of the structure reconstruction problem in single-particle cryo-electron microscopy, the chief motivation of this study. We focus on high noise regimes, where accurate detection of image occurrences within a measurement is impossible. To estimate the image, we develop an expectation-maximization framework that aims to maximize an approximation of the likelihood function. We demonstrate image recovery in highly noisy environments, and show that our framework outperforms the previously studied autocorrelation analysis in a wide range of parameters.
我们考虑二维多目标检测（MTD）问题，即从包含图像多个副本的噪声测量中估计目标图像，每个副本都经过随机旋转和平移。MTD模型是单颗粒冷冻电子显微镜中结构重建问题的数学抽象，也是本研究的主要动机。我们关注高噪声情况，在这种情况下，无法在测量中准确检测图像出现的位置。为了估计图像，我们开发了一个期望最大化框架，旨在最大化似然函数的近似值。我们展示了在高噪声环境下的图像恢复，并表明我们的框架在广泛的参数范围内优于先前研究的自相关分析。
