Callenberg, Clara; Lyons, Ashley; den Brok, Dennis; Fatima, Areeba; Turpin, Alex; Zickus, Vytautas; Machesky, Laura; Whitelaw, Jamie; Faccio, Daniele; Hulin, Matthias B.: Super-resolution time-resolved imaging using computational sensor fusion. In: Scientific reports2021, 11, 1689.
Online-Ausgabe in bonndoc:
author = {{Clara Callenberg} and {Ashley Lyons} and {Dennis den Brok} and {Areeba Fatima} and {Alex Turpin} and {Vytautas Zickus} and {Laura Machesky} and {Jamie Whitelaw} and {Daniele Faccio} and {Matthias B. Hulin}},
title = {Super-resolution time-resolved imaging using computational sensor fusion},
publisher = {Macmillan Publishers Limited, part of Springer Nature},
year = 2021,
month = jan,

journal = {Scientific reports},
volume = 2021, 11,
number = 1689,
note = {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 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 12 × 12 , and demonstrating up to 4 × 4 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.},
url = {}

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