Turbulence-immune computational ghost imaging based on a multi-scale generative adversarial network

TL;DR

This paper introduces a turbulence-immune computational ghost imaging method using a multi-scale generative adversarial network, significantly improving image quality under turbulent conditions without altering the traditional CGI framework.

Contribution

The novel integration of MsGAN into CGI to achieve turbulence immunity while maintaining the conventional framework is the key innovation.

Findings

Enhanced turbulence-free ghost image quality

Significant visual improvement in turbulent conditions

Effective optimization of the CGI measurement algorithm

Abstract

There is a consensus that turbulence-free images cannot be obtained by conventional computational ghost imaging (CGI) because the CGI is only a classic simulation, which does not satisfy the conditions of turbulence-free imaging. In this article, we first report a turbulence-immune CGI method based on a multi-scale generative adversarial network (MsGAN). Here, the conventional CGI framework is not changed, but the conventional CGI coincidence measurement algorithm is optimized by an MsGAN. Thus, the satisfactory turbulence-free ghost image can be reconstructed by training the network, and the visual effect can be significantly improved.