Review of deep learning based de-noising algorithms for phase imaging and applications to high-speed coherent imaging

Silvio Montresor; Marie Tahon; Pascal Picart; Pascal Picart

doi:10.1364/DH.2021.DTh1D.1

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper DTh1D.1
•https://doi.org/10.1364/DH.2021.DTh1D.1

Review of deep learning based de-noising algorithms for phase imaging and applications to high-speed coherent imaging

Silvio Montresor, Marie Tahon, and Pascal Picart

Digital Holography and Three-Dimensional Imaging 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-89-7

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Deep Learning, Neural Networks and Holographic Processing I (DTh1D)

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S. Montresor, M. Tahon, and P. Picart, "Review of deep learning based de-noising algorithms for phase imaging and applications to high-speed coherent imaging," in OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP), H. Hua, B. Javidi, M. Martinez-Corral, O. Matoba, A. Stern, S. Thibault, T. Alieva, J. Ke, F. Willomitzer, F. Okten, P. Silveira, P. Banerjee, E. Stoykova, D. Chu, J. Park, F. Imai, C. Joo, M. Digman, D. Gardner, S. Gladysz, D. LeMaster, S. Basu, and O. Korotkova, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper DTh1D.1.

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Abstract

We present a review of deep-learning based algorithms dedicated to the processing of the speckle noise in phase imaging with a focus on the decorrelation phase noise. Applications to high-speed coherent imaging are discussed.

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