Analysis of Phase-Extraction Neural Network (PhENN) performance for lensless quantitative phase imaging

Shuai Li; George Barbastathis; Alexandre Goy

doi:10.1117/12.2513310

4 March 2019 Analysis of Phase-Extraction Neural Network (PhENN) performance for lensless quantitative phase imaging

Shuai Li, George Barbastathis, Alexandre Goy

Proceedings Volume 10887, Quantitative Phase Imaging V; 108870T (2019) https://doi.org/10.1117/12.2513310
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Shuai Li, George Barbastathis, and Alexandre Goy "Analysis of Phase-Extraction Neural Network (PhENN) performance for lensless quantitative phase imaging", Proc. SPIE 10887, Quantitative Phase Imaging V, 108870T (4 March 2019); https://doi.org/10.1117/12.2513310

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