We consider the problem of spectral compressed sensing in continuous domain, which aims to recover a 2-dimensional spectrally sparse signal from partially observed time samples. The signal is assumed to be a superposition of s complex sinusoids. We propose a semidefinite program for the 2D signal recovery problem. Our model is able to handle large scale 2D signals of size 500 × 500, whereas traditional approaches only handle signals of size around 20 × 20.
|Original language||English (US)|
|Title of host publication||2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||5|
|State||Published - Jun 20 2017|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OCRF-2014-CRG-3
Acknowledgements: JFC is supported in part by Grant 16300616 of Hong Kong Research Grants Council. Weiyu Xu is supported by the Simons Foundation 318608 , KAUST OCRF-2014-CRG-3, NSF DMS-1418737 and NIH lROlEB020665-01
This publication acknowledges KAUST support, but has no KAUST affiliated authors.