Turbulence-immune computational ghost imaging based on a multi-scale generative adversarial network
Hao Zhang, Deyang Duan
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 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 ghost image can be reconstructed by training the network, and the visual effect can be significantly improved.
Topics & Concepts
TurbulenceGhost imagingGenerative adversarial networkComputer scienceScale (ratio)Generative grammarOpticsAdversarial systemArtificial intelligenceAlgorithmImage (mathematics)Computer visionPhysicsQuantum mechanicsThermodynamicsRandom lasers and scattering mediaAdvanced Optical Imaging TechnologiesDigital Media Forensic Detection