Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography

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We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concluded by applying our technique to reduce the temporally changing noise emerging from focal drift in time-lapse imaging of biological cells. This reduction cannot be performed using other optical methods for denoising. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Publisher
OPTICAL SOC AMER
Issue Date
2019-02
Language
English
Article Type
Article
Citation

OPTICS EXPRESS, v.27, no.4, pp.4927 - 4943

ISSN
1094-4087
DOI
10.1364/OE.27.004927
URI
http://hdl.handle.net/10203/251800
Appears in Collection
PH-Journal Papers(저널논문)
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