# Single-Pixel Pattern Recognition with Coherent Nonlinear Optics

arXiv: https://arxiv.org/abs/2010.02273 · HTML: https://mlsanity.com/paper/2010.02273/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Image-free single-pixel segmentation](https://mlsanity.com/paper/2108.10617/) (2021)
- [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)

We propose and experimentally demonstrate a nonlinear-optics approach to pattern recognition with single-pixel imaging and deep neural network. It employs mode selective image up-conversion to project a raw image onto a set of coherent spatial modes, whereby its signature features are extracted nonlinear-optically. With 40 projection modes, the classification accuracy reaches a high value of 99.49% for the MNIST handwritten digit images, and up to 95.32% even when they are mixed with strong noise. Our experiment harnesses rich coherent processes in nonlinear optics for efficient machine learning, with potential applications in online classification of large size images, fast lidar data analyses, complex pattern recognition, and so on.