This is not the latest version of this item. The latest version can be found here.
Analysis of Phase-Extraction Neural Network (PhENN) performance for lensless quantitative phase imaging
Name
108870T.pdf
Description
Published version
Size
4.53 MB
Format
Adobe PDF
Checksum (MD5)
8b61d735d222cde1141cb3efb5fc61cd
Author(s)
Li, Shuai
Barbastathis, George
Goy, Alexandre
Date Issued
March 4, 2019
Journal
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Publisher
SPIE
Citation
Li, Shuai, Barbastathis, George and Goy, Alexandre. 2019. "Analysis of Phase-Extraction Neural Network (PhENN) performance for lensless quantitative phase imaging." Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 10887.
Version
Final published version
Abstract
© 2019 SPIE. PhENN is a convolutional deep neural network that reconstructs quantitative phase images from diffracted intensity measurements some distance away from the phase objects. PhENN is trained on known phase-intensity pairs created from a particular database (e.g. ImageNet) but then found to perform well on objects created from other databases (e.g. Faces-LFW, MNIST, etc.). In this paper, we analyze the dependence of quantitative phase measurement quality on PhENN's architecture and the layout of the lensless imaging system, in particular, the number of layers (depth), the size of the innermost layer (waist size), the presence or absence of skip connections, the choice of training loss function and the free space propagation distance.
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
10.1117/12.2513310
