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

arXiv: https://arxiv.org/abs/2107.07870 · HTML: https://mlsanity.com/paper/2107.07870/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [Large model enhanced computational ghost imaging](https://mlsanity.com/paper/2503.08710/) (2025)

## Abstract (arXiv)

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.