PRIME: Phase Retrieval via Majorization-Minimization

This paper considers the phase retrieval problem in which measurements consist of only the magnitude of several linear measurements of the unknown, e.g., spectral components of a time sequence. We develop low-complexity algorithms with superior performance based on the majorization-minimization (MM) framework. The proposed algorithms are referred to as PRIME: Phase Retrieval vIa the Majorization-minimization techniquE. They are preferred to existing benchmark methods since at each iteration a simple surrogate problem is solved with a closed-form solution that monotonically decreases the original objective function. In total, three algorithms are proposed using different majorization-minimization techniques. Experimental results validate that our algorithms outperform existing methods in terms of successful recovery and mean-square error under various settings.

• Publication year

  2015

• Venue

  IEEE Transactions on Signal Processing

• Publication date

  2015-11-05

• Fields of study

  Mathematics, Computer Science, Engineering

• Identifiers
  DOI 10.1109/TSP.2016.2585084arXiv 1511.01669
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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