High-quality direct ghost imaging of random dynamic targets based on convolutional neural network

Abstract

We propose a novel high-quality direct random dynamic target ghost imaging method, based on Convolutional Neural Networks (CNNs). This method requires input of one-dimensional bucket detector values collected during the motion of the target object, enabling direct reconstruction of the target image. The results demonstrate the method’s capability to achieve high-quality reconstructed images for moving targets across various sampling rates. Furthermore, the method effectively addresses the challenge of image quality degradation caused by target motion. This contributes to the potential practical application value of ghost imaging in fields such as remote sensing and radar.