Phaseless super-resolution using masks

Kishore Jaganathan, James Saunderson, Maryam Fazei, Yonina C. Eldar, Babak Hassibi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of 2016 IEEE International Conference on Acoustics, Speech and Signal Processing
EditorsP.C. Ching, Dominic K.C. Ho
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Print)9781479999880
Publication statusPublished - 18 May 2016
Externally publishedYes
EventIEEE International Conference on Acoustics, Speech and Signal Processing 2016 - Shanghai International Convention Center, Shanghai, China
Duration: 20 Mar 201625 Mar 2016 (Proceedings)


ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing 2016
Abbreviated titleICASSP 2016
Internet address


  • Masks
  • Minimum separation
  • Phaseless super-resolution
  • Semidefinite relaxation

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