HoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network

Reconstruction of 3D scenes from digital holograms is an important task in different areas of science, such as biology, medicine, ecology, etc. A lot of parameters, such as the object’s shape, number, position, rate and density, can be extracted. However, reconstruction of off-axis and especially in...

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Bibliographic Details
Published in:Applied sciences 2023-05, Vol.13 (10), p.6125
Main Authors: Svistunov, Andrey S., Rymov, Dmitry A., Starikov, Rostislav S., Cheremkhin, Pavel A.
Format: Article
Language:English
Subjects:
Analysis
computer-generated holography
Data compression
digital holography
Holograms
Image processing
Image quality
image reconstruction
machine learning
neural network
Neural networks
Optical noise
Planes
spatial light modulator
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Summary:Reconstruction of 3D scenes from digital holograms is an important task in different areas of science, such as biology, medicine, ecology, etc. A lot of parameters, such as the object’s shape, number, position, rate and density, can be extracted. However, reconstruction of off-axis and especially inline holograms can be challenging due to the presence of optical noise, zero-order image and twin image. We have used a deep-multibranch neural network model, which we call HoloForkNet, to reconstruct different 2D sections of a 3D scene from a single inline hologram. This paper describes the proposed method and analyzes its performance for different types of objects. Both computer-generated and optically registered digital holograms with resolutions up to 2048 × 2048 pixels were reconstructed. High-quality image reconstruction for scenes consisting of up to eight planes was achieved. The average structural similarity index (SSIM) for 3D test scenes with eight object planes was 0.94. The HoloForkNet can be used to reconstruct 3D scenes consisting of micro- and macro-objects.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13106125