Robust data analysis and imaging with computational ghost imaging

TL;DR

This paper demonstrates that computational ghost imaging can effectively analyze and visualize digital data, showing robustness against noise and interference, with promising applications in various scientific and industrial fields.

Contribution

It introduces the use of computational ghost imaging for digital data analysis, highlighting its robustness and potential in big data and scientific applications.

Findings

Effective imaging of digital data characteristics like periodicity.

Robustness against strong noise and interference.

Potential applications in diverse fields such as meteorology and astronomy.

Abstract

Nowadays the world has entered into the digital age, in which the data analysis and visualization have become more and more important. In analogy to imaging the real object, we demonstrate that the computational ghost imaging can image the digital data to show their characteristics, such as periodicity. Furthermore, our experimental results show that the use of optical imaging methods to analyse data exhibits unique advantages, especially in anti-interference. The data analysis with computational ghost imaging can be well performed against strong noise, random amplitude and phase changes in the binarized signals. Such robust data data analysis and imaging has an important application prospect in big data analysis, meteorology, astronomy, economics and many other fields.