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

arXiv: https://arxiv.org/abs/2008.06842 · HTML: https://mlsanity.com/paper/2008.06842/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Physics-Informed Untrained Learning for RGB-Guided Superresolution Single-Pixel Hyperspectral Imaging](https://mlsanity.com/paper/2604.03572/) (2026)

## Abstract (arXiv)

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.