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Phaseless super-resolution using masks

Jaganathan, Kishore and Saunderson, James and Fazel, Maryam and Eldar, Yonina C. and Hassibi, Babak (2016) Phaseless super-resolution using masks. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE , Piscataway, NJ, pp. 4039-4043. ISBN 978-1-4799-9988-0.

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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.

Item Type:Book Section
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Additional Information:© 2016 IEEE.
Subject Keywords:Phaseless super-resolution, masks, minimum separation, semidefinite relaxation
Record Number:CaltechAUTHORS:20160523-154036848
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Official Citation:K. Jaganathan, J. Saunderson, M. Fazei, Y. C. Eldar and B. Hassibi, "Phaseless super-resolution using masks," 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 2016, pp. 4039-4043. doi: 10.1109/ICASSP.2016.7472436
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:67270
Deposited By: Tony Diaz
Deposited On:23 May 2016 22:50
Last Modified:03 Oct 2019 10:04

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