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10 March 2020 Experimental digital Gabor hologram rendering of C. elegans worms by a model-trained convolutional neural network (Conference Presentation)
Michael Atlan, Julie Rivet, Antoine Taliercio, Nicolas Boutry, Guillaume Tochon, Jean-Pierre Huignard
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Proceedings Volume 11251, Label-free Biomedical Imaging and Sensing (LBIS) 2020; 112511P (2020) https://doi.org/10.1117/12.2545514
Event: SPIE BiOS, 2020, San Francisco, California, United States
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.
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Michael Atlan, Julie Rivet, Antoine Taliercio, Nicolas Boutry, Guillaume Tochon, and Jean-Pierre Huignard "Experimental digital Gabor hologram rendering of C. elegans worms by a model-trained convolutional neural network (Conference Presentation)", Proc. SPIE 11251, Label-free Biomedical Imaging and Sensing (LBIS) 2020, 112511P (10 March 2020); https://doi.org/10.1117/12.2545514
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KEYWORDS
Digital holography
Holograms
Convolutional neural networks
Databases
Digital imaging
Image sensors
Data modeling
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