Deep learning for digital holography: a review
Recent years have witnessed the unprecedented progress of deep learning applications in
digital holography (DH). Nevertheless, there remain huge potentials in how deep learning …
digital holography (DH). Nevertheless, there remain huge potentials in how deep learning …
Optical wafer defect inspection at the 10 nm technology node and beyond
The growing demand for electronic devices, smart devices, and the Internet of Things
constitutes the primary driving force for marching down the path of decreased critical …
constitutes the primary driving force for marching down the path of decreased critical …
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 …
[HTML][HTML] Iterative projection meets sparsity regularization: towards practical single-shot quantitative phase imaging with in-line holography
Holography provides access to the optical phase. The emerging compressive phase
retrieval approach can achieve in-line holographic imaging beyond the information-theoretic …
retrieval approach can achieve in-line holographic imaging beyond the information-theoretic …
Time-multiplexed neural holography: a flexible framework for holographic near-eye displays with fast heavily-quantized spatial light modulators
Holographic near-eye displays offer unprecedented capabilities for virtual and augmented
reality systems, including perceptually important focus cues. Although artificial intelligence …
reality systems, including perceptually important focus cues. Although artificial intelligence …
[HTML][HTML] Deep-learning computational holography: A review
Deep learning has been developing rapidly, and many holographic applications have been
investigated using deep learning. They have shown that deep learning can outperform …
investigated using deep learning. They have shown that deep learning can outperform …
[HTML][HTML] Self-supervised learning of hologram reconstruction using physics consistency
Existing applications of deep learning in computational imaging and microscopy mostly
depend on supervised learning, requiring large-scale, diverse and labelled training data …
depend on supervised learning, requiring large-scale, diverse and labelled training data …
Deep learning-based incoherent holographic camera enabling acquisition of real-world holograms for holographic streaming system
While recent research has shown that holographic displays can represent photorealistic 3D
holograms in real time, the difficulty in acquiring high-quality real-world holograms has …
holograms in real time, the difficulty in acquiring high-quality real-world holograms has …
pyDHM: A Python library for applications in digital holographic microscopy
pyDHM is an open-source Python library aimed at Digital Holographic Microscopy (DHM)
applications. The pyDHM is a user-friendly library written in the robust programming …
applications. The pyDHM is a user-friendly library written in the robust programming …
3D single shot lensless incoherent optical imaging using coded phase aperture system with point response of scattered airy beams
Interferenceless coded aperture correlation holography (I-COACH) techniques have
revolutionized the field of incoherent imaging, offering multidimensional imaging capabilities …
revolutionized the field of incoherent imaging, offering multidimensional imaging capabilities …