Noise influence on DeepDensity: convolutional neural network for local fringe density map estimation

Maria Cywińska; Filip Brzeski; Wiktor Krajnik; Krzysztof Patorski; Maciej Trusiak

doi:10.1364/DH.2021.DTh1D.5

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper DTh1D.5
•https://doi.org/10.1364/DH.2021.DTh1D.5

Noise influence on DeepDensity: convolutional neural network for local fringe density map estimation

Maria Cywińska, Filip Brzeski, Wiktor Krajnik, Krzysztof Patorski, and Maciej Trusiak

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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M. Cywińska, F. Brzeski, W. Krajnik, K. Patorski, and M. Trusiak, "Noise influence on DeepDensity: convolutional neural network for local fringe density map estimation," 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.5.

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Abstract

Convolutional neural network based, fast and accurate local fringe density map estimation by DeepDensity significantly enhances full-field optical measurement techniques, e.g., holographic microscopy. The numerical capabilities of the proposed algorithmic solution in the case of the presence of noise were studied.

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