Learning-based method for speckle noise reduction of numerically reconstructed holograms without recording experimental datasets for its training

S. Jurado-Torres; C. Trujillo

doi:10.1364/DH.2021.DTh1D.6

OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)
OSA Technical Digest (Optica Publishing Group, 2021),
paper DTh1D.6
•https://doi.org/10.1364/DH.2021.DTh1D.6

Learning-based method for speckle noise reduction of numerically reconstructed holograms without recording experimental datasets for its training

S. Jurado-Torres and C. Trujillo

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. Jurado-Torres and C. Trujillo, "Learning-based method for speckle noise reduction of numerically reconstructed holograms without recording experimental datasets for its training," 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.6.

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Abstract

A convolutional autoencoder for speckle noise reduction of numerically reconstructed holograms is presented. The neural network is trained with a generic open dataset whose images are preprocessed to emulate speckle noise. Experimental validation is provided.

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