Group frame neural network of moving object ghost imaging combined with frame merging algorithm

TL;DR

This paper introduces a neural network and frame merging algorithm to enhance ghost imaging of moving objects, reducing sampling requirements and eliminating motion blur for clearer reconstructions.

Contribution

It proposes a novel multi-to-one neural network with batch frame concept and a frame merging algorithm to improve ghost imaging of moving objects.

Findings

Reduced sampling ratio in ghost imaging.

Elimination of motion blur in high-speed object imaging.

Consistent experimental and simulation results.

Abstract

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.