Nonconvex optimization meets low-rank matrix factorization: An overview
Substantial progress has been made recently on develo** provably accurate and efficient
algorithms for low-rank matrix factorization via nonconvex optimization. While conventional …
algorithms for low-rank matrix factorization via nonconvex optimization. While conventional …
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 …
Neural holography with camera-in-the-loop training
Holographic displays promise unprecedented capabilities for direct-view displays as well as
virtual and augmented reality applications. However, one of the biggest challenges for …
virtual and augmented reality applications. However, one of the biggest challenges for …
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 …
Phase retrieval via Wirtinger flow: Theory and algorithms
We study the problem of recovering the phase from magnitude measurements; specifically,
we wish to reconstruct a complex-valued signal about which we have phaseless samples of …
we wish to reconstruct a complex-valued signal about which we have phaseless samples of …
End-to-end learning of 3d phase-only holograms for holographic display
Computer-generated holography (CGH) provides volumetric control of coherent wavefront
and is fundamental to applications such as volumetric 3D displays, lithography, neural …
and is fundamental to applications such as volumetric 3D displays, lithography, neural …
Phase imaging with an untrained neural network
Most of the neural networks proposed so far for computational imaging (CI) in optics employ
a supervised training strategy, and thus need a large training set to optimize their weights …
a supervised training strategy, and thus need a large training set to optimize their weights …
Kramers–Kronig coherent receiver
The interest for short-reach links of the kind needed for inter-data-center communications
has fueled in recent years the search for transmission schemes that are simultaneously …
has fueled in recent years the search for transmission schemes that are simultaneously …
A geometric analysis of phase retrieval
Can we recover a complex signal from its Fourier magnitudes? More generally, given a set
of m measurements, y_k=\left| a _k^* x\right| yk= ak∗ x for k= 1, ..., mk= 1,…, m, is it possible …
of m measurements, y_k=\left| a _k^* x\right| yk= ak∗ x for k= 1, ..., mk= 1,…, m, is it possible …
Spectral methods for data science: A statistical perspective
Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …