On the use of deep learning for phase recovery
Phase recovery (PR) refers to calculating the phase of the light field from its intensity
measurements. As exemplified from quantitative phase imaging and coherent diffraction …
measurements. As exemplified from quantitative phase imaging and coherent diffraction …
[HTML][HTML] Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications
Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle
and light sources to experimentally study sub-nanosecond transients or dynamic processes …
and light sources to experimentally study sub-nanosecond transients or dynamic processes …
Physics-driven universal twin-image removal network for digital in-line holographic microscopy
Digital in-line holographic microscopy (DIHM) enables efficient and cost-effective
computational quantitative phase imaging with a large field of view, making it valuable for …
computational quantitative phase imaging with a large field of view, making it valuable for …
HoloForkNet: digital hologram reconstruction via multibranch neural network
AS Svistunov, DA Rymov, RS Starikov… - Applied Sciences, 2023 - mdpi.com
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 …
science, such as biology, medicine, ecology, etc. A lot of parameters, such as the object's …
Wrapped phase aberration compensation using deep learning in digital holographic microscopy
In digital holographic microscopy (DHM), phase aberration compensation is a general
problem for improving the accuracy of quantitative phase measurement. Current phase …
problem for improving the accuracy of quantitative phase measurement. Current phase …
Single-shot inline holography using a physics-aware diffusion model
Among holographic imaging configurations, inline holography excels in its compact design
and portability, making it the preferred choice for on-site or field applications with unique …
and portability, making it the preferred choice for on-site or field applications with unique …
Generative adversarial neural network for 3D-hologram reconstruction
SA Kiriy, DA Rymov, AS Svistunov… - Laser Physics …, 2024 - iopscience.iop.org
Neural-network-based reconstruction of digital holograms can improve the speed and the
quality of micro-and macro-object images, as well as reduce the noise and suppress the twin …
quality of micro-and macro-object images, as well as reduce the noise and suppress the twin …
End-to-end infrared radiation sensing technique based on holography-guided visual attention network
Infrared radiation imaging is extensively utilized due to its unique advantages in terms of
wavelengths. This paper presents an end-to-end short-wave infrared digital holography …
wavelengths. This paper presents an end-to-end short-wave infrared digital holography …
[HTML][HTML] Untrained network regularized by total variation in single-shot lensless holography
Y Feng, J Xu, J Jiao, L Zhong, X Lu, J Tian - Results in Physics, 2023 - Elsevier
The optical complex-amplitude (CA) distribution of an object contains rich information,
providing insights into the object's optical characteristics such as retardation and absorption …
providing insights into the object's optical characteristics such as retardation and absorption …
Dual-constrained physics-enhanced untrained neural network for lensless imaging
Z Wang, S Zheng, Z Ding, C Guo - JOSA A, 2024 - opg.optica.org
An untrained neural network (UNN) paves a new way to realize lensless imaging from single-
frame intensity data. Based on the physics engine, such methods utilize the smoothness …
frame intensity data. Based on the physics engine, such methods utilize the smoothness …