Image estimation based on depth-variant imaging model in three-dimensional microscopy

被引：1

作者：

Tao, QC ^{[1
]}

He, XH ^{[1
]}

Zhao, J ^{[1
]}

Teng, QZ ^{[1
]}

Chen, JG ^{[1
]}

机构：

[1] Sichuan Univ, Coll Elect Informat, Chengdu 610064, Peoples R China

来源：

Electronic Imaging and Multimedia Technology IV | 2005年 / 5637卷

关键词：

optical sections microscopy; image restoration; depth-variant PSF; maximum-likelihood estimation; expectation maximization;

D O I：

10.1117/12.577515

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An algorithm for maximum-likelihood image restoration based on the expectation maximization (EM) algorithm is proposed in this paper. This estimation is based on a depth-variant imaging model in three-dimensional optical sectioning microscopy. As a result of the refractive index mismatch between the immersion medium and the mounting medium of the specimen, the imaging model in three-dimensional optical-sectioning microscopy incorporates spherical aberration that worsens with increasing depth under the coverslip and changes in the point spread function (PSF). Two-dimension images restoration and three-dimension serial images restoration are to be used to analyze the capability of the EM-ML algorithm, and the performance shows that the EM-NIL algorithm can restore the blurred of image by the depth variant image model.

引用

页码：590 / 598

页数：9

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