Ghost imaging target classification through deep sequential feature extraction

Abstract

In Computational Ghost Imaging (CGI), the bucket signals play a crucial role, as they capture the encoded information about the object, enabling the reconstruction of images even without traditional detectors. By analyzing the bucket signals, it is possible to classify the target using image-free GI. This paper integrates Long Short-Term Memory (LSTM) networks into CGI, leveraging their gated mechanisms to filter noise, capture sequential features, and extract global object-specific information. The proposed method is evaluated through both simulation and physical experiments. Simulation results show a classification accuracy of 91 % at a sampling rate of 5 %. Additionally, we conducted robustness experiments by introducing Gaussian noise to the input data, under which the LSTM model maintained relatively high accuracy compared to baseline methods. Furthermore, physical experiments validate the feasibility of the approach and demonstrate stable classification performance under real-world conditions, confirming its potential for practical low-sampling, image-free recognition applications.