Large Scale 2D Spectral Compressed Sensing in Continuous Domain
arXiv:1903.00767 · doi:10.1109/ICASSP.2017.7953289
Abstract
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.
5 pages, 2 figures, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)