# Experimental comparison of single-pixel imaging algorithms

arXiv: https://arxiv.org/abs/1707.03164 · HTML: https://mlsanity.com/paper/1707.03164/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [Image-free single-pixel segmentation](https://mlsanity.com/paper/2108.10617/) (2021)
- [Image-free multi-character recognition](https://mlsanity.com/paper/2112.10587/) (2021)
- [Weighted Encoding Optimization for Dynamic Single-pixel Imaging and Sensing](https://mlsanity.com/paper/2201.02833/) (2022)
- [Deep-learned orthogonal basis patterns for fast, noise-robust single-pixel imaging](https://mlsanity.com/paper/2205.08736/) (2022)
- [PISE: Physics-Anchored Semantically-Enhanced Deep Computational Ghost Imaging for Robust Low-Bandwidth Machine Perception](https://mlsanity.com/paper/2601.12551/) (2026)

## Abstract (arXiv)

Single-pixel imaging (SPI) is a novel technique capturing 2D images using a photodiode, instead of conventional 2D array sensors. SPI owns high signal-to-noise ratio, wide spectrum range, low cost, and robustness to light scattering. Various algorithms have been proposed for SPI reconstruction, including the linear correlation methods, the alternating projection method (AP), and the compressive sensing based methods. However, there has been no comprehensive review discussing respective advantages, which is important for SPI's further applications and development. In this paper, we reviewed and compared these algorithms in a unified reconstruction framework. Besides, we proposed two other SPI algorithms including a conjugate gradient descent based method (CGD) and a Poisson maximum likelihood based method. Both simulations and experiments validate the following conclusions: to obtain comparable reconstruction accuracy, the compressive sensing based total variation regularization method (TV) requires the least measurements and consumes the least running time for small-scale reconstruction; the CGD and AP methods run fastest in large-scale cases; the TV and AP methods are the most robust to measurement noise. In a word, there are trade-offs between capture efficiency, computational complexity and robustness to noise among different SPI algorithms. We have released our source code for non-commercial use.