Super-resolution time-resolved imaging using computational sensor fusion.
Super-resolution time-resolved imaging using computational sensor fusion.
复制标题
使用计算传感器融合的超分辨率时间分辨成像。
DOI：
10.1038/s41598-021-81159-x
复制
发表时间：
2021-01-18
影响因子：
4.6
通讯作者：
Hullin MB
中科院分区：
文献类型：
--
作者：
Callenberg C;Lyons A;Brok DD;Fatima A;Turpin A;Zickus V;Machesky L;Whitelaw J;Faccio D;Hullin MB
在所有三个坐标中具有高精度的场景的整个横向空间和时间维度上的成像是从LIDAR到荧光寿命成像的应用的关键。然而，通常需要以牺牲时间分辨率为代价牺牲例如空间分辨率的折衷，特别是当在短采集时间内需要完整的3维数据立方体时。我们介绍了一种传感器融合的方法，结合数据具有低的空间分辨率，但高的时间精度收集与单光子雪崩二极管（SPAD）阵列的数据，具有高的空间分辨率，但没有时间分辨率，如与标准的CMOS相机获得。我们的方法，模糊的SPAD阵列和计算传感器融合的图像的基础上，重建时间分辨的图像在显着更高的空间分辨率比SPAD输入，上采样数值数据的一个因素，并展示了上采样的实验数据。我们展示了激光雷达应用和荧光癌细胞FLIM的技术。该技术为高空间分辨率SPAD成像铺平了道路，或者等效地，FLIM成像与传统显微镜在帧速率加速超过一个数量级。
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 , and demonstrating up to 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.
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影响因子：
5.3
作者：
Itoh, RE;Kurokawa, K;Matsuda, M
通讯作者：
Matsuda, M
影响因子：
3.8
作者：
Buttafava, Mauro;Zeman, Jessica;Velten, Andreas
通讯作者：
Velten, Andreas
影响因子：
16.6
作者：
Velten, Andreas;Willwacher, Thomas;Raskar, Ramesh
通讯作者：
Raskar, Ramesh
影响因子：
1.3
作者：
Dowling, K;Dayel, MJ;Dymoke-Bradshaw, AKL
通讯作者：
Dymoke-Bradshaw, AKL
DOI：
10.1007/978-1-84800-155-8_7
发表时间：
2008-01-01
期刊：
RECENT ADVANCES IN LEARNING AND CONTROL
影响因子：
--
作者：
Grant, Michael C.;Boyd, Stephen P.
通讯作者：
Boyd, Stephen P.