# Robust and Efficient Single-Pixel Image Classificationwith Nonlinear Optics

arXiv: https://arxiv.org/abs/2101.11696 · HTML: https://mlsanity.com/paper/2101.11696/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2021.

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

- [Image-free single-pixel segmentation](https://mlsanity.com/paper/2108.10617/) (2021)
- [Enhancing Multistep Prediction of Multivariate Market Indices Using Weighted Optical Reservoir Computing](https://mlsanity.com/paper/2408.00652/) (2024)

## Abstract (arXiv)

We present a hybrid image classifier by mode-selective image upconversion, single pixel photodetection, and deep learning, aiming at fast processing a large number of pixels. It utilizes partial Fourier transform to extract the signature features of images in both the original and Fourier domains, thereby significantly increasing the classification accuracy and robustness. Tested on the MNIST handwritten digit images, it boosts the accuracy from 81.25% to 99.23%, and achieves an 83% accuracy for highly contaminated images whose signal-to-noise ratio is only -17 dB. Our approach could prove useful for fast lidar data processing, high resolution image recognition, occluded target identification, atmosphere monitoring, and so on.