Least squares phase retrieval using feasible point pursuit

Cheng Qian; Nicholas D. Sidiropoulos; Kejun Huang; Lei Huang; Hing-Cheung So

ICASSP 2016accepted0 citations

Least squares phase retrieval using feasible point pursuit

Cheng Qian, Nicholas D. Sidiropoulos, Kejun Huang, Lei Huang, Hing-Cheung So

Abstract

Phase retrieval has recently attracted renewed interest. It is revisited here through a new approach based on nonconvex quadratically constrained quadratic programming (QCQP). A least-squares (LS) formulation is adopted, and a recently developed non-convex QCQP approximation technique called feasible point pursuit (FPP) is tailored to obtain a new LS-FPP phase retrieval algorithm. The Cramér-Rao bound (CRB) is also derived for phase retrieval under additive white Gaussian noise. We demonstrate through simulations that the LS-FPP method outperforms the prior art and its mean square error approaches the CRB.

BibTeX

@inproceedings{icassp2016_leastsquaresphas,
  title = {Least squares phase retrieval using feasible point pursuit},
  author = {Cheng Qian and Nicholas D. Sidiropoulos and Kejun Huang and Lei Huang and Hing-Cheung So},
  booktitle = {ICASSP 2016},
  year = {2016}
}