Deep S$^3$PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models
It addresses a largely unsolved problem in domains like microscopy and wireless communication, but appears incremental as it applies a known deep learning approach to a specific bottleneck.
The paper tackles the simultaneous source separation and phase retrieval (S^3PR) problem, which is under-determined and non-convex, by restricting solutions to a deep generative model's range, enabling its solution.
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.