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2002.05856
Cited By

Deep S$^3$PR: Simultaneous Source Separation and Phase Retrieval Using
Deep Generative Models

v1v2 (latest)

Deep S $^3$ PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020

14 February 2020

Christopher A. Metzler

Gordon Wetzstein

ArXiv (abs)PDF HTML

Papers citing "Deep S$^3$PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models"

4 / 4 papers shown

SimPINNs: Simulation-Driven Physics-Informed Neural Networks for
Enhanced Performance in Nonlinear Inverse Problems

SimPINNs: Simulation-Driven Physics-Informed Neural Networks for Enhanced Performance in Nonlinear Inverse ProblemsWorkshop on Hyperspectral Image and Signal Processing (WHISPERS), 2023

124

2

0

27 Sep 2023

TurbuGAN: An Adversarial Learning Approach to Spatially-Varying
Multiframe Blind Deconvolution with Applications to Imaging Through
Turbulence

TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through TurbulenceIEEE Journal on Selected Areas in Information Theory (JSAIT), 2022

Brandon Yushan Feng

Christopher A. Metzler

397

18

0

13 Mar 2022

$Diffractive optical system design by cascaded propagation$

Diffractive optical system design by cascaded propagationOptics Express (OE), 2022

137

15

0

23 Feb 2022

Deep Learning Techniques for Inverse Problems in Imaging

Deep Learning Techniques for Inverse Problems in Imaging

Christopher A. Metzler

Richard G. Baraniuk

Rebecca Willett

494

640

0

12 May 2020

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