Depth-variant maximum-likelihood restoration for three-dimensional fluorescence microscopy
Depth-variant maximum-likelihood restoration for three-dimensional fluorescence microscopy
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DOI：
10.1364/josaa.21.001593
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发表时间：
2004-09-01
影响因子：
1.9
通讯作者：
Conchello, JA
中科院分区：
文献类型：
--
作者：
Preza, C;Conchello, JA
我们通过使用新的近似模型来基于预期最大化（EM）形式，用于最大样品图像估计的算法，以进行光学分段显微镜进行深度变化的图像形成。这种新的基于地层的模型结合了球形像差，随着显微镜在盖玻片下更深地集中在盖子上，这是浸入式介质与样品的安装介质之间的折射率不匹配的结果。具有已知几何形状和折射率的样品的图像表明，该模型捕获了图像的主要特征。我们通过模拟分析了深度变化EM算法的性能，这表明该算法可以通过深度来补偿图像降解的变化。 （c）2004美国光学学会。
We derive an algorithm for maximum-likelihood image estimation on the basis of the expectation-maximization (EM) formalism by using a new approximate model for depth-varying image formation for optical sectioning microscopy. This new strata-based model incorporates spherical aberration that worsens as the microscope is focused deeper under the cover slip and is the result of the refractive-index mismatch between the immersion medium and the mounting medium of the specimen. Images of a specimen with known geometry and refractive index show that the model captures the main features of the image. We analyze the performance of the depth-variant EM algorithm with simulations, which show that the algorithm can compensate for image degradation changing with depth. (C) 2004 Optical Society of America.