Phaseless super-resolution using masks
Kishore Jaganathan, James Saunderson, Maryam Fazel, Yonina C. Eldar, Babak Hassibi
Abstract
Phaseless super-resolution is the problem of reconstructing a signal from its low-frequency Fourier magnitude measurements. It is the combination of two classic signal processing problems: phase retrieval and super-resolution. Due to the absence of phase and high-frequency measurements, additional information is required in order to be able to uniquely reconstruct the signal of interest. In this work, we use masks to introduce redundancy in the phaseless measurements. We develop an analysis framework for this setup, and use it to show that any super-resolution algorithm can be seamlessly extended to solve phaseless superresolution (up to a global phase), when measurements are obtained using a certain set of masks. In particular, we focus our attention on a robust semidefinite relaxation-based algorithm, and provide reconstruction guarantees. Numerical simulations complement our theoretical analysis.
BibTeX
@inproceedings{icassp2016_phaselesssuperre,
title = {Phaseless super-resolution using masks},
author = {Kishore Jaganathan and James Saunderson and Maryam Fazel and Yonina C. Eldar and Babak Hassibi},
booktitle = {ICASSP 2016},
year = {2016}
}