Super-Resolution Time-Resolved Imaging using Computational Sensor Fusion

TL;DR

This paper presents a sensor fusion method combining low-resolution, high-temporal-resolution SPAD data with high-resolution, low-temporal-resolution images to reconstruct high-resolution, time-resolved 3D images rapidly, benefiting applications like LIDAR and fluorescence imaging.

Contribution

The authors introduce a novel computational sensor fusion technique that significantly enhances spatial resolution in time-resolved imaging by combining different sensor data types.

Findings

Achieves 12x12 upsampling of data

Demonstrates 4x4 upsampling in experiments

Enables high-resolution, fast time-resolved imaging

Abstract

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.