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A model to achieve high-resolution three-dimensional microscopic images from synthetically generated digital holograms by using Convolutional Neural Networks (CNNs) is proposed. By employing low-cost microscopy systems and computational techniques, we demonstrate that proposed model provides viable alternative to costly high-resolution microscopic systems. Specifically, the study focuses on the elimination of the unwanted terms in backward-propagated holograms to closely approximate original high-resolution objects.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Bora Duman andG. Bora Esmer
"Removal of unwanted terms from single shot in-line digital holograms by convolutional neural network", Proc. SPIE 12996, Unconventional Optical Imaging IV, 129960G (18 June 2024); https://doi.org/10.1117/12.3017233
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Bora Duman, G. Bora Esmer, "Removal of unwanted terms from single shot in-line digital holograms by convolutional neural network," Proc. SPIE 12996, Unconventional Optical Imaging IV, 129960G (18 June 2024); https://doi.org/10.1117/12.3017233