Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization. [PDF]
Light Sci Appl, 2022 Deep learning-based image reconstruction methods have achieved remarkable success in phase recovery and holographic imaging. However, the generalization of their image reconstruction performance to new types of samples never seen by the network remains a
Chen H, Huang L, Liu T, Ozcan A.
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HoloPhaseNet: fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model. [PDF]
Biomed Opt Express, 2022 Quantitative phase imaging with off-axis digital holography in a microscopic configuration provides insight into the cells' intracellular content and morphology. This imaging is conventionally achieved by numerical reconstruction of the recorded hologram,
Jaferzadeh K, Fevens T.
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Acoustic Hologram Reconstruction With Unsupervised Neural Network
Frontiers in Materials, 2022 An acoustic hologram is crucial in various acoustics applications. The reconstruction accuracy of the acoustic field from the hologram is important for determining the performance of the acoustic hologram system.
Boyi Li +6 more
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Inline hologram reconstruction with sparsity constraints [PDF]
Optics Letters, 2009 Inline digital holograms are classically reconstructed using linear operators to model diffraction. It has long been recognized that such reconstruction operators do not invert the hologram formation operator. Classical linear reconstructions yield images with artifacts such as distortions near the field-of-view boundaries or twin images.
Denis, Loïc +4 more
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On Data Selection and Regularization for Underdetermined Vibro-Acoustic Source Identification [PDF]
SensorsThe number of hologram points in near-field acoustical holography (NAH) for a vibro-acoustic system plays a vital role in conditioning the transfer function between the source and measuring points.
Laixu Jiang +3 more
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Enhancing light efficiency in phase-only holograms via neural network [PDF]
Scientific ReportsArtificial neural networks have emerged as powerful tools for hologram synthesis and reconstruction, offering improvements in both image quality and computational efficiency.
Balakiruthika Periyasamy +2 more
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Orbital angular momentum- and frequency-dependent high-capacity encrypted hologram through multi-dimensional multiplexing acoustic metasurface [PDF]
Nature CommunicationsSound holography has shown great capability in reconstructing the arbitrary complex sound fields, with potential applications in various scenarios. To overcome the limitations of existing technologies in terms of capacity and security, here we propose ...
Yong-qiang Zhou +6 more
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Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network. [PDF]
Nat CommunRecent advances in deep learning-based image reconstruction techniques have led to significant progress in phase retrieval using digital in-line holographic microscopy (DIHM).
Kim J, Kim Y, Lee HS, Seo E, Lee SJ.
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Self-supervised learning of hologram reconstruction using physics consistency [PDF]
Nature Machine Intelligence, 2022 Existing applications of deep learning in computational imaging and microscopy mostly depend on supervised learning, requiring large-scale, diverse and labelled training data.
Luzhe Huang +3 more
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HoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network
Applied Sciences, 2023 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,
A. S. Svistunov +3 more
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