Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery
Holography encodes the three-dimensional (3D) information of a sample in the form of an
intensity-only recording. However, to decode the original sample image from its hologram …
intensity-only recording. However, to decode the original sample image from its hologram …
Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks
Digital holography is one of the most widely used label-free microscopy techniques in
biomedical imaging. Recovery of the missing phase information on a hologram is an …
biomedical imaging. Recovery of the missing phase information on a hologram is an …
Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram
Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a
single-snapshot hologram. However, unlike a conventional bright-field microscopy image …
single-snapshot hologram. However, unlike a conventional bright-field microscopy image …
Phase recovery and holographic image reconstruction using deep learning in neural networks
Phase recovery from intensity-only measurements forms the heart of coherent imaging
techniques and holography. In this study, we demonstrate that a neural network can learn to …
techniques and holography. In this study, we demonstrate that a neural network can learn to …
Fast phase retrieval in off-axis digital holographic microscopy through deep learning
G Zhang, T Guan, Z Shen, X Wang, T Hu, D Wang… - Optics express, 2018 - opg.optica.org
Traditional digital holographic imaging algorithms need multiple iterations to obtain focused
reconstructed image, which is time-consuming. In terms of phase retrieval, there is also the …
reconstructed image, which is time-consuming. In terms of phase retrieval, there is also the …
End-to-end deep learning framework for digital holographic reconstruction
Digital holography records the entire wavefront of an object, including amplitude and phase.
To reconstruct the object numerically, we can backpropagate the hologram with Fresnel …
To reconstruct the object numerically, we can backpropagate the hologram with Fresnel …
Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
Deep learning-based image reconstruction methods have achieved remarkable success in
phase recovery and holographic imaging. However, the generalization of their image …
phase recovery and holographic imaging. However, the generalization of their image …
Fringe pattern improvement and super-resolution using deep learning in digital holography
Digital holographic imaging is a powerful technique that can provide wavefront information
of a three-dimensional object for biological and industrial applications. However, due to the …
of a three-dimensional object for biological and industrial applications. However, due to the …
Deep DIH: single-shot digital in-line holography reconstruction by deep learning
Digital in-line holography (DIH) is broadly used to reconstruct 3D shapes of microscopic
objects from their 2D holograms. One of the technical challenges in the reconstruction stage …
objects from their 2D holograms. One of the technical challenges in the reconstruction stage …
eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction
It is well known that in-line digital holography (DH) makes use of the full pixel count in
forming the holographic imaging. But it usually requires phase-shifting or phase retrieval …
forming the holographic imaging. But it usually requires phase-shifting or phase retrieval …