Multiple illumination phaseless super-resolution (MIPS) with applications to phaseless DoA estimation and diffraction imaging

Phaseless super-resolution is the problem of recovering an unknown signal from measurements of the “magnitudes” of the “low frequency” Fourier transform of the signal. This problem arises in applications where measuring the phase, and making high-frequency measurements, are either too costly or altogether infeasible. The problem is especially challenging because it combines the difficult problems of phase retrieval and classical super-resolution. Recently, the authors in [1] demonstrated that by making three phaseless low-frequency measurements, obtained by appropriately “masking” the signal, one can uniquely and robustly identify the phase using convex programming and obtain the same super-resolution performance reported in [2]. However, the masks proposed in [1] are very specific and in many applications cannot be directly implemented. In this paper, we broadly extend the class of masks that can be used to recover the phase and show how their effect can be emulated in coherent diffraction imaging using multiple illuminations, as well as in direction-of-arrival (DoA) estimation using multiple sources to excite the environment. We provide numerical simulations to demonstrate the efficacy of the method and approach.

[1]  David P. Williamson,et al.  Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming , 1995, JACM.

[2]  Emmanuel J. Candès,et al.  PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming , 2011, ArXiv.

[3]  A. Walther The Question of Phase Retrieval in Optics , 1963 .

[4]  Haim Azhari,et al.  Super-resolution in PET imaging , 2006, IEEE Transactions on Medical Imaging.

[5]  K. Puschmann,et al.  On super-resolution in astronomical imaging , 2005 .

[6]  Parikshit Shah,et al.  Compressed Sensing Off the Grid , 2012, IEEE Transactions on Information Theory.

[7]  J R Fienup,et al.  Phase retrieval algorithms: a comparison. , 1982, Applied optics.

[8]  P. P. Vaidyanathan,et al.  Sparse Sensing With Co-Prime Samplers and Arrays , 2011, IEEE Transactions on Signal Processing.

[9]  O. Bunk,et al.  Ptychographic X-ray computed tomography at the nanoscale , 2010, Nature.

[10]  Justin K. Romberg,et al.  Efficient Compressive Phase Retrieval with Constrained Sensing Vectors , 2015, NIPS.

[11]  T. Engin Tuncer,et al.  Classical and Modern Direction-of-Arrival Estimation , 2009 .

[12]  Yonina C. Eldar,et al.  Phase retrieval with masks using convex optimization , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).

[13]  P. P. Vaidyanathan,et al.  Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom , 2010, IEEE Transactions on Signal Processing.

[14]  Babak Hassibi,et al.  Sparse phase retrieval: Convex algorithms and limitations , 2013, 2013 IEEE International Symposium on Information Theory.

[15]  Yonina C. Eldar,et al.  Phase Retrieval with Application to Optical Imaging: A contemporary overview , 2015, IEEE Signal Processing Magazine.

[16]  R. Balan,et al.  On signal reconstruction without phase , 2006 .

[17]  Kishore Jaganathan,et al.  Convex Programming-Based Phase Retrieval: Theory and Applications , 2016 .

[18]  Alexandre d'Aspremont,et al.  Phase recovery, MaxCut and complex semidefinite programming , 2012, Math. Program..

[19]  Yonina C. Eldar,et al.  Phaseless super-resolution using masks , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Thomas Kailath,et al.  ESPRIT-estimation of signal parameters via rotational invariance techniques , 1989, IEEE Trans. Acoust. Speech Signal Process..

[21]  Hayit Greenspan,et al.  Super-Resolution in Medical Imaging , 2009, Comput. J..

[22]  Emmanuel J. Cand Towards a Mathematical Theory of Super-Resolution , 2012 .

[23]  Zhang Fe Phase retrieval from coded diffraction patterns , 2015 .

[24]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[25]  Yonina C. Eldar,et al.  Phase Retrieval: An Overview of Recent Developments , 2015, ArXiv.

[26]  Rick P. Millane,et al.  Phase retrieval in crystallography and optics , 1990 .

[27]  Yonina C. Eldar,et al.  Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices , 2012, IEEE Transactions on Information Theory.

[28]  Yonina C. Eldar,et al.  Phase Retrieval with Application to Optical Imaging , 2014, ArXiv.