Deep S^3PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models
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This paper introduces and solves the simultaneous source separation and phase retrieval (S^3PR) problem. S^3PR shows up in a number application domains, most notably computational optics, where one has multiple independent coherent sources whose phase is difficult to measure. In general, S^3PR 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^3PR.
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