# Optical machine learning with incoherent light and a single-pixel detector

arXiv: https://arxiv.org/abs/1904.10851 · HTML: https://mlsanity.com/paper/1904.10851/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 8 of 8)

- [Classification and reconstruction of spatially overlapping phase images using diffractive optical networks](https://mlsanity.com/paper/2108.07977/) (2021)
- [Deep-learning-driven Reliable Single-pixel Imaging with Uncertainty Approximation](https://mlsanity.com/paper/2107.11678/) (2021)
- [Image-free single-pixel segmentation](https://mlsanity.com/paper/2108.10617/) (2021)
- [Classification and reconstruction for single-pixel imaging with classical and quantum neural networks](https://mlsanity.com/paper/2407.12506/) (2024)
- [Statistical optimality conditions for compressive ensembles](https://mlsanity.com/paper/2106.01092/) (2021)
- [Adversarial attacks on an optical neural network](https://mlsanity.com/paper/2205.01226/) (2022)
- [Playing Tic-Tac-Toe Games with Intelligent Single-pixel Imaging](https://mlsanity.com/paper/2205.03663/) (2022)
- [Solving combinational optimization problems with evolutionary single-pixel imaging](https://mlsanity.com/paper/2210.05923/) (2022)

## Abstract (arXiv)

An optical diffractive neural network (DNN) can be implemented with a cascaded phase mask architecture. Like an optical computer, the system can perform machine learning tasks such as number digit recognition in an all-optical manner. However, the system can only work under coherent light illumination and the precision requirement in practical experiments is quite high. This paper proposes an optical machine learning framework based on single-pixel imaging (MLSPI). The MLSPI system can perform the same linear pattern recognition task as DNN. Furthermore, it can work under incoherent lighting conditions, has lower experimental complexity and can be easily programmable.