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Proceedings Paper

Experimental digital Gabor hologram rendering of C. elegans worms by a model-trained convolutional neural network (Conference Presentation)

Paper Abstract

Digital magnitude image rendering in Gabor holography can be performed by a convolutional neural network trained with a fully synthetic database formed by image pairs generated randomly. These pairs are linked by a numerical model propagation of a scalar wave field from the object to the sensor array. The synthetic database is formed by generating images made from source points at random locations with random brightness on a black background. Successful prediction of experimental Gabor holograms of microscopic worms by a UNet trained with 50,000 random image pairs is achieved, and a classifier-based regularization for twin-image removal is investigated.

Paper Details

Date Published: 10 March 2020
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Proc. SPIE 11251, Label-free Biomedical Imaging and Sensing (LBIS) 2020, 112511P (10 March 2020); doi: 10.1117/12.2545514
Show Author Affiliations
Michael Atlan, Institut Langevin Ondes et Images (France)
Julie Rivet, Institut Langevin Ondes et Images (France)
Antoine Taliercio, Institut Langevin Ondes et Images (France)
Nicolas Boutry, EPITA (France)
Guillaume Tochon, EPITA (France)
Jean-Pierre Huignard, Institut Langevin Ondes et Images (France)


Published in SPIE Proceedings Vol. 11251:
Label-free Biomedical Imaging and Sensing (LBIS) 2020
Natan T. Shaked; Oliver Hayden, Editor(s)

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