Deep S3PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models
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Abstract (arXiv)
This paper introduces and solves the simultaneous source separation and phase retrieval (S3PR) problem. S3PR is an important but largely unsolved problem in a number application domains, including microscopy, wireless communication, and imaging through scattering media, where one has multiple independent coherent sources whose phase is difficult to measure. In general, S3PR is highly under-determined, non-convex, and difficult to solve. In this work, we demonstrate that by restricting the solutions to lie in the range of a deep generative model, we can constrain the search space sufficiently to solve S3PR.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Feb 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2022TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulencecited by 11
- 2023SimPINNs: Simulation-Driven Physics-Informed Neural Networks for Enhanced Performance in Nonlinear Inverse Problemscited by 1
- 2024Low-light phase retrieval with implicit generative priorscited by 1
- 2024Low-light phase retrieval with implicit generative priors27 Feb 2024
- 2023SimPINNs: Simulation-Driven Physics-Informed Neural Networks for Enhanced Performance in Nonlinear Inverse Problems27 Sep 2023
- 2022TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence13 Mar 2022