Deep learning in computational microscopy
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Published version
Date
2019
Authors
Nguyen, Thanh
Nehmetallah, George
Tian, Lei
Version
Published version
OA Version
Citation
Thanh Nguyen, George Nehmetallah, Lei Tian. 2019. "Deep learning in computational microscopy." Computational Imaging IV. Computational Imaging IV. https://doi.org/10.1117/12.2520089
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.
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Copyright 2019 Society of Photo Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibited.