# Single Pixel Image Classification using an Ultrafast Digital Light Projector

arXiv: https://arxiv.org/abs/2603.12036 · HTML: https://mlsanity.com/paper/2603.12036/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Selective Overview of Deep Learning](https://mlsanity.com/paper/1904.05526/) (2019)
- [Non-attracting Regions of Local Minima in Deep and Wide Neural Networks](https://mlsanity.com/paper/1812.06486/) (2018)
- [Classification and reconstruction for single-pixel imaging with classical and quantum neural networks](https://mlsanity.com/paper/2407.12506/) (2024)
- [Fast Object Classification in Single-pixel Imaging](https://mlsanity.com/paper/1805.07582/) (2018)
- [Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies](https://mlsanity.com/paper/2411.04354/) (2024)

## Abstract (arXiv)

Pattern recognition and image classification are essential tasks in machine vision. Autonomous vehicles, for example, require being able to collect the complex information contained in a changing environment and classify it in real time. Here, we experimentally demonstrate image classification at multi-kHz frame rates combining the technique of single pixel imaging (SPI) with a low complexity machine learning model. The use of a microLED-on-CMOS digital light projector for SPI enables ultrafast pattern generation for sub-ms image encoding. We investigate the classification accuracy of our experimental system against the broadly accepted benchmarking task of the MNIST digits classification. We compare the classification performance of two machine learning models: An extreme learning machine (ELM) and a backpropagation trained deep neural network. The complexity of both models is kept low so the overhead added to the inference time is comparable to the image generation time. Crucially, our single pixel image classification approach is based on a spatiotemporal transformation of the information, entirely bypassing the need for image reconstruction. By exploring the performance of our SPI based ELM as binary classifier we demonstrate its potential for efficient anomaly detection in ultrafast imaging scenarios.