Digital Hologram Denoising Using Conditional Generative Adversarial Networks

Siddharth Rawat; Anna Wang

doi:10.1364/DH.2021.DM5E.6

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper DM5E.6
•https://doi.org/10.1364/DH.2021.DM5E.6

Digital Hologram Denoising Using Conditional Generative Adversarial Networks

Siddharth Rawat and Anna Wang

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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Digital Holographic Microscopy I (DM5E)

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S. Rawat and A. Wang, "Digital Hologram Denoising Using Conditional Generative Adversarial Networks," 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 DM5E.6.

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Abstract

Accurate feature extraction from digitally acquired in-line and off-axis holograms using analytical methods is challenging in the presence of noise. We present a strategy to overcome this limitation by using conditional generative adversarial networks (cGANs).

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