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数字全息

数字全息技术是一种结合了全息学和数字图像处理技术的先进成像技术。它通过记录物体的光波信息,尤其是物体与参考光波的干涉图样,来重建物体的三维信息。与传统全息技术不同,数字全息不依赖于传统的光学显像板或胶片,而是使用数字相机和计算机进行光波信息的记录与重建,因此具有许多独特的优势。

数字全息的基本原理与传统全息相似,都是通过记录物体反射或透过光波的相位和振幅信息来再现物体的三维图像。在传统的全息成像中,利用激光光源照射到物体上,再通过干涉板将物体的反射光与参考光进行干涉,最终得到全息图。而数字全息则是通过数字探测器,如CCD(电荷耦合器件)或CMOS(互补金属氧化物半导体)传感器,直接记录干涉图样,并通过计算机进行处理和重建。

代表性论文

  • Fully forward mode training for optical neural networks, Nature 2024

  • Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence, Science 2024

  • Photonic neuromorphic architecture for tens-of-task lifelong learning, Light: Science & Applications 2024

  • All-analog photo-electronic chip for high-speed vision tasks, Nature 2023

  • Photonic unsupervised learning variational autoencoder for high-throughput and low-latency image transmission, Science Advances 2023

  • Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning, Nature Communications 2023

  • Ultrafast dynamic machine vision with spatiotemporal photonic computing, Science Advances 2023

  • A multichannel optical computing architecture for advanced machine vision, Light: Science & Applications 2022

  • Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit, Nature Photonics 2021

  • In situ optical backpropagation training of diffractive optical neural networks, Photonics Research 2020

  • Fourier-space diffractive deep neural network, Physical Review Letters 2019