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.
@article{arxiv.1903.00767,
title = {Large Scale 2D Spectral Compressed Sensing in Continuous Domain},
author = {Jian-Feng Cai and Weiyu Xu and Yang Yang},
journal= {arXiv preprint arXiv:1903.00767},
year = {2019}
}
Comments
5 pages, 2 figures, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)