# Self-evolving ghost imaging

arXiv: https://arxiv.org/abs/2008.00648 · HTML: https://mlsanity.com/paper/2008.00648/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [Solving combinational optimization problems with evolutionary single-pixel imaging](https://mlsanity.com/paper/2210.05923/) (2022)
- [Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering media](https://mlsanity.com/paper/2511.22913/) (2025)

## Abstract (arXiv)

Ghost imaging can capture 2D images with a point detector instead of an array sensor. It therefore offers a solution to the challenge of building area format sensors in wavebands where such sensors are difficult and expensive to produce and opens up new imaging modalities due to high-performance single-pixel detectors. Traditionally, ghost imaging retrieves the image of an object offline, by correlating measured light intensities and applied illuminating patterns. Here we present a feedback-based approach for online updating of the imaging result that can bypass post-processing, termed self-evolving ghost imaging (SEGI). We introduce a genetic algorithm to optimize the illumination patterns in real-time to match the objects shape according to the measured total light intensity. We theoretically and experimentally demonstrate this concept for static and dynamic imaging. This method opens new perspectives for real-time ghost imaging in applications such as remote sensing (e.g. machine vision, LiDAR systems in autonomous vehicles) and biological imaging.