论文标题
深s $^3 $ pr：使用深生成模型的同时源分离和相位检索
Deep S$^3$PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models
论文作者
论文摘要
本文介绍并解决了同时的源分离和相位检索（S $^3 $ PR）问题。 S $^3 $ PR是一个重要但在很大程度上无法解决的问题，包括显微镜，无线通信和通过散射介质进行成像，在该介质中，一个人具有难以测量的多个独立的相干来源。通常，S $^3 $ PR是高度确定的，非凸且难以解决的。在这项工作中，我们证明，通过将解决方案限制在深层生成模型范围内，我们可以充分限制搜索空间以求解S $^3 $ pr。
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.
