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arXiv 1802.00432cs.ITeess.SPmath.IT
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PhaseLin:线性相位恢复

PhaseLin: Linear Phase Retrieval

Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer

摘要:

相位恢复处理从幅度测量恢复复值或实值信号。如最近所示,PhaseMax方法通过凸优化实现相位恢复,而不会将问题提升到更高的维度。为了成功,PhaseMax需要对解进行初始猜测,这可以通过频谱初始化器来计算。在本文中,我们表明,与初始猜测的可用性,相位恢复可以进行一个更简单的,线性的过程。我们的算法,称为PhaseLin,是线性估计,最小化均方误差(MSE)时,适用于幅度测量。PhaseLin的线性性质使得能够对任意测量矩阵进行精确和非渐近的MSE分析。我们还证明,通过迭代使用相位林,一个到达一个有效的相位恢复算法,执行与现有的凸和非凸方法的合成和真实世界的数据。

英文摘要:

Phase retrieval deals with the recovery of complex- or real-valued signals from magnitude measurements. As shown recently, the method PhaseMax enables phase retrieval via convex optimization and without lifting the problem to a higher dimension. To succeed, PhaseMax requires an initial guess of the solution, which can be calculated via spectral initializers. In this paper, we show that with the availability of an initial guess, phase retrieval can be carried out with an ever simpler, linear procedure. Our algorithm, called PhaseLin, is the linear estimator that minimizes the mean squared error (MSE) when applied to the magnitude measurements. The linear nature of PhaseLin enables an exact and nonasymptotic MSE analysis for arbitrary measurement matrices. We furthermore demonstrate that by iteratively using PhaseLin, one arrives at an efficient phase retrieval algorithm that performs on par with existing convex and nonconvex methods on synthetic and real-world data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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