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Proceedings Paper

Deep learning in computational microscopy
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Paper Abstract

We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging. Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.

Paper Details

Date Published: 13 May 2019
PDF: 11 pages
Proc. SPIE 10990, Computational Imaging IV, 1099007 (13 May 2019); doi: 10.1117/12.2520089
Show Author Affiliations
Thanh Nguyen, The Catholic Univ. of America (United States)
George Nehmetallah, The Catholic Univ. of America (United States)
Lei Tian, Boston Univ. (United States)

Published in SPIE Proceedings Vol. 10990:
Computational Imaging IV
Abhijit Mahalanobis; Lei Tian; Jonathan C. Petruccelli, Editor(s)

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