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.
Cai J-F, Xu W, Yang Y (2017) Large scale 2D spectral compressed sensing in continuous domain. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Available: http://dx.doi.org/10.1109/icassp.2017.7953289.
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