End-to-end computational ghost imaging method that suppresses atmospheric turbulence

Images are one of the important sources of getting information, and the process of getting images can be affected by various factors. Atmospheric turbulence is one of them. Ghost imaging has a positive effect on suppressing atmos-pheric turbulence, but its reconstruction results are not stable, and it cannot get high-quality images under extreme conditions. In this paper, we simulate atmospheric turbulence using a phase screen, combine computational ghost imaging to simulate the imaging process, and analyze the factors that affect the imaging. We use an end-to-end neu-ral network to input the bucket signal into the network after processing, which can not only reconstruct the target image directly but also save reconstruction time by removing the process of correlation calculation. Simulations show that good reconstruction results can be obtained at low sampling rates and extreme conditions. (c) 2023 Optica Publishing Group

Automated summary

This paper investigates the impact of atmospheric turbulence on the image acquisition process and uses computational ghost imaging to simulate the process. The study finds that good reconstruction results can be obtained using an end-to-end neural network at low sampling rates and extreme conditions.