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CPRL - An Extension of Compressive Sensing to the Phase Retrieval Problem

机译:CPRL-将压缩感测扩展到相位检索问题

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While compressive sensing (CS) has been one of the most vibrant research fields in the past few years, most development only applies to linear models. This limits its application in many areas where CS could make a difference. This paper presents a novel extension of CS to the phase retrieval problem, where intensity measurements of a linear system are used to recover a complex sparse signal. We propose a novel solution using a lifting technique - CPRL, which relaxes the NP-hard problem to a nonsmooth semidefinite program. Our analysis shows that CPRL inherits many desirable properties from CS, such as guarantees for exact recovery. We further provide scalable numerical solvers to accelerate its implementation.
机译:尽管压缩感测(CS)在过去几年中是最活跃的研究领域之一,但大多数开发仅适用于线性模型。这限制了它在CS可能会有所作为的许多领域中的应用。本文提出了将CS扩展到相位恢复问题的新方法,其中线性系统的强度测量用于恢复复杂的稀疏信号。我们提出了一种使用提升技术-CPRL的新颖解决方案,它将NP-hard问题松弛为一个非光滑的半确定程序。我们的分析表明,CPRL从CS继承了许多理想的属性,例如保证精确恢复。我们进一步提供可扩展的数值求解器,以加快其实现速度。

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