Caramazza Piergiorgio, Moran Oisín, Murray-Smith Roderick, Faccio Daniele
School of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, UK.
School of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK.
Nat Commun. 2019 May 2;10(1):2029. doi: 10.1038/s41467-019-10057-8.
The optical transport of images through a multimode fibre remains an outstanding challenge with applications ranging from optical communications to neuro-imaging. State of the art approaches either involve measurement and control of the full complex field transmitted through the fibre or, more recently, training of artificial neural networks that however, are typically limited to image classes belong to the same class as the training data set. Here we implement a method that statistically reconstructs the inverse transformation matrix for the fibre. We demonstrate imaging at high frame rates, high resolutions and in full colour of natural scenes, thus demonstrating general-purpose imaging capability. Real-time imaging over long fibre lengths opens alternative routes to exploitation for example for secure communication systems, novel remote imaging devices, quantum state control processing and endoscopy.
通过多模光纤进行图像的光学传输仍然是一个重大挑战,其应用涵盖从光通信到神经成像等多个领域。目前的先进方法要么涉及对通过光纤传输的完整复场进行测量和控制,要么是最近对人工神经网络进行训练,然而,这些方法通常仅限于与训练数据集属于同一类别的图像类别。在此,我们实现了一种方法,该方法可对光纤的逆变换矩阵进行统计重建。我们展示了在高帧率、高分辨率下对自然场景进行全彩色成像的能力,从而证明了其通用成像能力。在长光纤长度上的实时成像为例如安全通信系统、新型远程成像设备、量子态控制处理和内窥镜检查等应用开辟了新途径。