Learning-based nonparametric autofocusing for digital holography
In digital holography, it is crucial to extract the object distance from a hologram in order to
reconstruct its amplitude and phase. This is known as autofocusing, which is conventionally …
reconstruct its amplitude and phase. This is known as autofocusing, which is conventionally …
Fast autofocusing in digital holography using the magnitude differential
Typical methods of automatic estimation of focusing in digital holography calculate every
single reconstructed frame to get a critical function and then ascertain the focal plane by …
single reconstructed frame to get a critical function and then ascertain the focal plane by …
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 …
Convolutional neural network-based regression for depth prediction in digital holography
Digital holography enables us to reconstruct objects in three-dimensional space from
holograms captured by an imaging device. For the reconstruction, we need to know the …
holograms captured by an imaging device. For the reconstruction, we need to know the …
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 …
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 …
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 …
Refocusing criterion via sparsity measurements in digital holography
Several automatic approaches have been proposed in the past to compute the refocus
distance in digital holography (DH). However most of them are based on a maximization or …
distance in digital holography (DH). However most of them are based on a maximization or …
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 …
Edge sparsity criterion for robust holographic autofocusing
Autofocusing is essential to digital holographic imaging. Previously used autofocusing
criteria exhibit challenges when applied to, eg, connected objects with different optical …
criteria exhibit challenges when applied to, eg, connected objects with different optical …