# Computational ghost imaging using deep learning

arXiv: https://arxiv.org/abs/1710.08343 · HTML: https://mlsanity.com/paper/1710.08343/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2017.

## Built on by (most cited first, 7 of 7)

- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [Large model enhanced computational ghost imaging](https://mlsanity.com/paper/2503.08710/) (2025)
- [Deep-learning-driven Reliable Single-pixel Imaging with Uncertainty Approximation](https://mlsanity.com/paper/2107.11678/) (2021)
- [Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging](https://mlsanity.com/paper/2104.02873/) (2021)
- [Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction](https://mlsanity.com/paper/2404.06657/) (2024)
- [Compressive sensing inspired self-supervised single-pixel imaging](https://mlsanity.com/paper/2603.29732/) (2026)
- [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) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two- or three- dimensional images with a single or a few bucket detectors, the quality of the reconstructed images is reduced by noise due to the reconstruction of images from random patterns. In this study, we improve the quality of CGI images using deep learning. A deep neural network is used to automatically learn the features of noise-contaminated CGI images. After training, the network is able to predict low-noise images from new noise-contaminated CGI images.