# Giga-voxel multidimensional fluorescence imaging combining single-pixel detection and data fusion

arXiv: https://arxiv.org/abs/2107.12337 · HTML: https://mlsanity.com/paper/2107.12337/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2021.

## Built on by (most cited first, 4 of 4)

- [Single pixel imaging at high pixel resolutions](https://mlsanity.com/paper/2206.02510/) (2022)
- [Single-sample image-fusion upsampling of fluorescence lifetime images](https://mlsanity.com/paper/2404.13102/) (2024)
- [Learning Binary Sampling Patterns for Single-Pixel Imaging using Bilevel Optimisation](https://mlsanity.com/paper/2508.19068/) (2025)
- [Learned Single-Pixel Fluorescence Microscopy](https://mlsanity.com/paper/2507.18740/) (2025)

## Abstract (arXiv)

Time-resolved fluorescence imaging is a key tool in biomedical applications, as it allows to non-invasively obtain functional and structural information. However, the big amount of collected data introduces challenges in both acquisition speed and processing needs. Here, we introduce a novel technique that allows to reconstruct a Giga-voxel 4D hypercube in a fast manner while only measuring 0.03 % of the information. The system combines two single-pixel cameras and a conventional 2D array detector working in parallel. Data fusion techniques are introduced to combine the individual 2D and 3D projections acquired by each sensor in the final high-resolution 4D hypercube, which can be used to identify different fluorophore species by their spectral and temporal signatures.