# Fast Computational Ghost Imaging using Unpaired Deep Learning and a Constrained Generative Adversarial Network

arXiv: https://arxiv.org/abs/2106.04822 · HTML: https://mlsanity.com/paper/2106.04822/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Phase recovery and holographic image reconstruction using deep learning in neural networks](https://mlsanity.com/paper/1705.04286/) (2017)
- [Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media](https://mlsanity.com/paper/1806.04139/) (2018)
- [Ghost imaging based on Y-net: a dynamic coding and conjugate-decoding approach](https://mlsanity.com/paper/2002.03824/) (2020)

## Abstract (arXiv)

The unpaired training can be the only option available for fast deep learning-based ghost imaging, where obtaining a high signal-to-noise ratio (SNR) image copy of each low SNR ghost image could be practically time-consuming and challenging. This paper explores the capabilities of deep learning to leverage computational ghost imaging when there is a lack of paired training images. The deep learning approach proposed here enables fast ghost imaging through reconstruction of high SNR images from faint and hastily shot ghost images using a constrained Wasserstein generative adversarial network. In the proposed approach, the objective function is regularized to enforce the generation of faithful and relevant high SNR images to the ghost copies. This regularization measures the distance between reconstructed images and the faint ghost images in a low-noise manifold generated by a shadow network. The performance of the constrained network is shown to be particularly important for ghost images with low SNR. The proposed pipeline is able to reconstruct high-quality images from the ghost images with SNR values not necessarily equal to the SNR of the training set.