Research on ghost imaging reconstruction by generative adversarial network and Rayleigh fading channel

In previous research on ghost imaging encoding transmission schemes, the influence of real transmission channels on the communication quality was weakened to some extent. Simultaneously, to ensure the imaging quality of the algorithm, it is often performed under full sampling or even supersampling, which undoubtedly requires a long sampling time. This paper proposes a ghost imaging reconstruction method that uses a generative adversarial network and Rayleigh fading channel. By introducing the channel transmission model (Rayleigh fading channel) in real scenes and the generative adversarial neural network model, the image is reconstructed under under-sampling and the imaging time is saved. To further explore how to improve the image transmission quality and reduce the channel interference as much as possible, this scheme provides a new imaging technology for the research of the image transmission field, which has good theoretical significance.