  The nature of multiple samples to extract correlation information limits the
applications of ghost imaging of moving objects. A novel multi-to-one neural
network is proposed and the concept of "batch frame" is introduced to improve
the serial imaging method. The neural network extracts more correlation
information from a small number of samples, thus reducing the sampling ratio of
the ghost imaging technique. We combine the correlation characteristics between
images to propose a frame merging algorithm, which eliminates the dynamic blur
of high-speed moving objects and further improves the reconstruction quality of
moving object images at a low sampling ratio. The experimental results are
consistent with the simulation results.
