# Super-Resolution Time-Resolved Imaging using Computational Sensor Fusion

arXiv: https://arxiv.org/abs/2101.03949 · HTML: https://mlsanity.com/paper/2101.03949/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2021.

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

- [Fast fluorescence lifetime imaging analysis via extreme learning machine](https://mlsanity.com/paper/2203.13754/) (2022)
- [Single-sample image-fusion upsampling of fluorescence lifetime images](https://mlsanity.com/paper/2404.13102/) (2024)

## Abstract (arXiv)

Imaging across both the full transverse spatial and temporal dimensions of a scene with high precision in all three coordinates is key to applications ranging from LIDAR to fluorescence lifetime imaging. However, compromises that sacrifice, for example, spatial resolution at the expense of temporal resolution are often required, in particular when the full 3-dimensional data cube is required in short acquisition times. We introduce a sensor fusion approach that combines data having low-spatial resolution but high temporal precision gathered with a single-photon-avalanche-diode (SPAD) array with set of data that has high spatial but no temporal resolution, such as that acquired with a standard CMOS camera. Our method, based on blurring the image on the SPAD array and computational sensor fusion, reconstructs time-resolved images at significantly higher spatial resolution than the SPAD input, upsampling numerical data by a factor 12x12, and demonstrating up to 4x4 upsampling of experimental data. We demonstrate the technique for both LIDAR applications and FLIM of fluorescent cancer cells. This technique paves the way to high spatial resolution SPAD imaging or, equivalently, FLIM imaging with conventional microscopes at frame rates accelerated by more than an order of magnitude.