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Classification of Holograms with 3D-CNN

A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed in different depths by using standard techniques of...

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Detalles Bibliográficos
Autores principales: Terbe, Dániel, Orzó, László, Zarándy, Ákos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654288/
https://www.ncbi.nlm.nih.gov/pubmed/36366064
http://dx.doi.org/10.3390/s22218366
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author Terbe, Dániel
Orzó, László
Zarándy, Ákos
author_facet Terbe, Dániel
Orzó, László
Zarándy, Ákos
author_sort Terbe, Dániel
collection PubMed
description A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed in different depths by using standard techniques of digital holography. We claim that a 2D convolutional network (CNN) cannot be efficient in decoding this volumetric information spread across the whole image as it inherently operates on local spatial features. Therefore, we propose a method, where we extract the volumetric information of the hologram by mapping it to a volume—using a standard wavefield propagation algorithm—and then feed it to a 3D-CNN-based architecture. We apply this method to a challenging real-life classification problem and compare its performance with an equivalent 2D-CNN counterpart. Furthermore, we inspect the robustness of the methods to slightly defocused inputs and find that the 3D method is inherently more robust in such cases. Additionally, we introduce a hologram-specific augmentation technique, called hologram defocus augmentation, that improves the performance of both methods for slightly defocused inputs. The proposed 3D-model outperforms the standard 2D method in classification accuracy both for in-focus and defocused input samples. Our results confirm and support our fundamental hypothesis that a 2D-CNN-based architecture is limited in the extraction of volumetric information globally encoded in the reconstructed hologram image.
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spelling pubmed-96542882022-11-15 Classification of Holograms with 3D-CNN Terbe, Dániel Orzó, László Zarándy, Ákos Sensors (Basel) Communication A hologram, measured by using appropriate coherent illumination, records all substantial volumetric information of the measured sample. It is encoded in its interference patterns and, from these, the image of the sample objects can be reconstructed in different depths by using standard techniques of digital holography. We claim that a 2D convolutional network (CNN) cannot be efficient in decoding this volumetric information spread across the whole image as it inherently operates on local spatial features. Therefore, we propose a method, where we extract the volumetric information of the hologram by mapping it to a volume—using a standard wavefield propagation algorithm—and then feed it to a 3D-CNN-based architecture. We apply this method to a challenging real-life classification problem and compare its performance with an equivalent 2D-CNN counterpart. Furthermore, we inspect the robustness of the methods to slightly defocused inputs and find that the 3D method is inherently more robust in such cases. Additionally, we introduce a hologram-specific augmentation technique, called hologram defocus augmentation, that improves the performance of both methods for slightly defocused inputs. The proposed 3D-model outperforms the standard 2D method in classification accuracy both for in-focus and defocused input samples. Our results confirm and support our fundamental hypothesis that a 2D-CNN-based architecture is limited in the extraction of volumetric information globally encoded in the reconstructed hologram image. MDPI 2022-10-31 /pmc/articles/PMC9654288/ /pubmed/36366064 http://dx.doi.org/10.3390/s22218366 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Terbe, Dániel
Orzó, László
Zarándy, Ákos
Classification of Holograms with 3D-CNN
title Classification of Holograms with 3D-CNN
title_full Classification of Holograms with 3D-CNN
title_fullStr Classification of Holograms with 3D-CNN
title_full_unstemmed Classification of Holograms with 3D-CNN
title_short Classification of Holograms with 3D-CNN
title_sort classification of holograms with 3d-cnn
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654288/
https://www.ncbi.nlm.nih.gov/pubmed/36366064
http://dx.doi.org/10.3390/s22218366
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