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

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.

5 pages, 6 figures

References in corpus (3)

• Lensless ghost imaging with true thermal light
• Color night vision correlation imaging without an infrared focal plane array
• Computational Ghost Imaging

Cited by in corpus (1)

• Can a conventional optical camera realize turbulence-free imaging?