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arXiv · 2107.07870

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

Hao Zhang·Deyang Duan
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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.

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Hao Zhang, Deyang Duan. 2021-07-14. Turbulence-immune computational ghost imaging based on a multi-scale generative adversarial network. https://doi.org/10.1364/oe.447301

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