3D computational microscopy with depth-varying point-spread functions using a principal component analysis method

Authors

Shuai Yuan, University of MemphisFollow
Chrysanthe Preza, University of Memphis

Abstract

An image estimation method based on a principle component analysis (PCA) model for the representation of the depth varying point spread function is presented and demonstrated with 3D simulated and experimental data. © 2013 Optical Society of America.

Publication Title

Optics InfoBase Conference Papers

Recommended Citation

Yuan, S., & Preza, C. (2013). 3D computational microscopy with depth-varying point-spread functions using a principal component analysis method. Optics InfoBase Conference Papers https://doi.org/10.1364/isa.2013.im3e.4