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
doaj +4 more sources
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.
europepmc +3 more sources
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.
europepmc +2 more sources
OAH-Net: a deep neural network for efficient and robust hologram reconstruction for off-axis digital holographic microscopy. [PDF]
Biomed Opt ExpressOff-axis digital holographic microscopy is a high-throughput, label-free imaging technology that provides three-dimensional, high-resolution information about samples, which is particularly useful in large-scale cellular imaging.
Liu W +9 more
europepmc +3 more sources
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
openaire +5 more sources
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
doaj +2 more sources
Extended Field of View and Resolution Enhancement in Lensless Digital Holography [PDF]
SensorsLensless digital holography provides a simple, low-cost imaging platform with a large field of view (FOV) and quantitative phase capability, making it attractive for biomedical imaging, microstructure inspection, and large area imaging.
Chung-Hsuan Huang +4 more
doaj +2 more sources
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
doaj +2 more sources
Hologram-Shifting Method for High-Speed Electron Hologram Reconstruction
Japanese Journal of Applied Physics, 1992 A simple digital method for high-speed or real-time reconstruction of electron holograms is described. The method is based on the so-called hologram shifting which causes a phase-shifting effect. Consequently the phase-shifting formula, which requires only some simple calculations, can in priciple be utilized to reconstruct electron holograms in ...
Qingxin Ru +3 more
openaire +2 more sources
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
semanticscholar +1 more source