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使用联合反卷积增强时间图像序列的恢复

Enhancing the recovery of a temporal sequence of images using joint deconvolution.

作者信息

Caramazza Piergiorgio, Wilson Kali, Gariepy Genevieve, Leach Jonathan, McLaughlin Stephen, Faccio Daniele, Altmann Yoann

机构信息

Institute of Photonics and Quantum Sciences, School of Engineering & Physical Sciences, Heriot-Watt University, EH14 4AS, Edinburgh, United Kingdom.

Institute of Sensors, Signals and Systems, School of Engineering & Physical Sciences, Heriot-Watt University, EH14 4AS, Edinburgh, United Kingdom.

出版信息

Sci Rep. 2018 Mar 27;8(1):5257. doi: 10.1038/s41598-018-22811-x.

Abstract

In this work, we address the reconstruction of spatial patterns that are encoded in light fields associated with a series of light pulses emitted by a laser source and imaged using photon-counting cameras, with an intrinsic response significantly longer than the pulse delay. Adopting a Bayesian approach, we propose and demonstrate experimentally a novel joint temporal deconvolution algorithm taking advantage of the fact that single pulses are observed simultaneously by different pixels. Using an intensified CCD camera with a 1000-ps gate, stepped with 10-ps increments, we show the ability to resolve images that are separated by a 10-ps delay, four time better compared to standard deconvolution techniques.

摘要

在这项工作中,我们致力于重建空间模式,这些模式编码在与激光源发射的一系列光脉冲相关的光场中,并使用光子计数相机进行成像,其固有响应时间明显长于脉冲延迟。采用贝叶斯方法,我们提出并通过实验证明了一种新颖的联合时间去卷积算法,该算法利用了不同像素同时观测单个脉冲这一事实。使用具有1000皮秒门控且步长为10皮秒的增强型电荷耦合器件相机,我们展示了分辨延迟为10皮秒的图像的能力,与标准去卷积技术相比,分辨率提高了四倍。

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