# Large Scale 2D Spectral Compressed Sensing in Continuous Domain

## TL;DR

This paper introduces a semidefinite programming approach for large-scale 2D spectral compressed sensing in the continuous domain, enabling recovery of high-resolution signals from limited samples.

## Contribution

It presents a novel semidefinite program that scales to 500x500 signals, significantly surpassing traditional methods in size handling.

## Key findings

- Successfully recovers 2D spectrally sparse signals from partial observations.
- Handles large-scale signals of size up to 500x500.
- Outperforms traditional methods limited to 20x20 signals.

## 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.