Randomness assisted in-line holography with deep learning.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
07 Jul 2023
Historique:
received: 02 04 2023
accepted: 28 06 2023
medline: 10 7 2023
pubmed: 8 7 2023
entrez: 7 7 2023
Statut: epublish

Résumé

We propose and demonstrate a holographic imaging scheme exploiting random illuminations for recording hologram and then applying numerical reconstruction and twin image removal. We use an in-line holographic geometry to record the hologram in terms of the second-order correlation and apply the numerical approach to reconstruct the recorded hologram. This strategy helps to reconstruct high-quality quantitative images in comparison to the conventional holography where the hologram is recorded in the intensity rather than the second-order intensity correlation. The twin image issue of the in-line holographic scheme is resolved by an unsupervised deep learning based method using an auto-encoder scheme. Proposed learning technique leverages the main characteristic of autoencoders to perform blind single-shot hologram reconstruction, and this does not require a dataset of samples with available ground truth for training and can reconstruct the hologram solely from the captured sample. Experimental results are presented for two objects, and a comparison of the reconstruction quality is given between the conventional inline holography and the one obtained with the proposed technique.

Identifiants

pubmed: 37419990
doi: 10.1038/s41598-023-37810-w
pii: 10.1038/s41598-023-37810-w
pmc: PMC10329003
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

10986

Subventions

Organisme : Science and Engineering Research Board
ID : CORE/2019/000026

Informations de copyright

© 2023. The Author(s).

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Auteurs

Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, 221005, India.

Aditya Chandra Mandal (AC)

Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, 221005, India.
Department of Mining Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, 221005, India.

Mohit Rathor (M)

Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, 221005, India.

Zeev Zalevsky (Z)

Faculty of Engineering and Nano Technology Center, Bar-Ilan University, Ramat Gan, Israel.

Rakesh Kumar Singh (RK)

Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, 221005, India. krakeshsingh.phy@iitbhu.ac.in.

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Classifications MeSH