High-speed computational ghost imaging with compressed sensing based on a convolutional neural network

TL;DR

This paper introduces a high-speed computational ghost imaging method that combines compressed sensing with a convolutional neural network to achieve faster image reconstruction with fewer samples.

Contribution

The novel scheme integrates CNN-based processing with compressed sensing to significantly improve the speed and efficiency of computational ghost imaging.

Findings

Produces high-quality images with fewer samples

Outperforms conventional CS and deep learning methods

Achieves faster imaging speed

Abstract

Computational ghost imaging (CGI) has recently been intensively studied as an indirect imaging technique. However, the speed of CGI cannot meet the requirements of practical applications. Here, we propose a novel CGI scheme for high-speed imaging. In our scenario, the conventional CGI data processing algorithm is optimized to a new compressed sensing (CS) algorithm based on a convolutional neural network (CNN). CS is used to process the data collected by a conventional CGI device. Then, the processed data are trained by a CNN to reconstruct the image. The experimental results show that our scheme can produce high-quality images with much less sampling than conventional CGI. Moreover, detailed comparisons between the images reconstructed using our approach and with conventional CS and deep learning (DL) show that our scheme outperforms the conventional approach and achieves a faster imaging speed.