  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.
