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

Qingchuan Tao D.V.M.; Xiaohai He; Jia Zhao; Qizhi Teng; Jianguo Chen

doi:10.1117/12.577515

8 February 2005 Image estimation based on depth-variant imaging model in three-dimensional microscopy

Qingchuan Tao D.V.M., Xiaohai He, Jia Zhao, Qizhi Teng, Jianguo Chen

Author Affiliations +

Proceedings Volume 5637, Electronic Imaging and Multimedia Technology IV; (2005) https://doi.org/10.1117/12.577515
Event: Photonics Asia, 2004, Beijing, China

Abstract

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-ML algorithm can restore the blurred of image by the depth variant image model.

Citation Download Citation

Qingchuan Tao D.V.M., Xiaohai He, Jia Zhao, Qizhi Teng, and Jianguo Chen "Image estimation based on depth-variant imaging model in three-dimensional microscopy", Proc. SPIE 5637, Electronic Imaging and Multimedia Technology IV, (8 February 2005); https://doi.org/10.1117/12.577515

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE