Deep S3PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models

Christopher A. Metzler; Gordon Wetzstein

ICASSP 2021accepted0 citations

Deep S3PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models

Christopher A. Metzler, Gordon Wetzstein

Abstract

This paper introduces and solves the simultaneous source separation and phase retrieval (S 3 PR) problem. S 3 PR 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, S 3 PR 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 S 3 PR.Code associated with this work is available at https://github.com/computational-imaging/DeepS3PR. An extended version of this work is available at https://arxiv.org/abs/2002.05856.

BibTeX

@inproceedings{icassp2021_deeps3prsimultan,
  title = {Deep S3PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models},
  author = {Christopher A. Metzler and Gordon Wetzstein},
  booktitle = {ICASSP 2021},
  year = {2021}
}