基于衍射网络的光谱编码单像素机器视觉
Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
摘要：
物质的3D工程为设计可以通过光-物质相互作用执行各种计算任务的系统开辟了新的途径。在这里，我们展示了以多个衍射层的形式设计的光学网络，这些衍射层使用深度学习进行训练，将物体的空间信息转换和编码为衍射光的功率谱，用于使用单像素光谱检测器对物体进行光学分类。使用具有基于等离子体纳米天线的检测器的时域光谱设置，我们在太赫兹光谱下实验验证了这种机器视觉框架，通过检测十个不同波长的衍射光的光谱功率来对手写数字的图像进行光学分类，每个波长代表一个类别/数字。我们还报告了通过衍射光学网络与浅电子神经网络实现的这种光谱编码的耦合，单独训练以仅基于衍射光内这十个不同波长中编码的光谱信息来重建手写数字的图像。这些重建图像展示了特定任务的图像解压缩，也可以作为新的输入循环回同一个衍射网络，以改善其光学对象分类。这种独特的机器视觉框架将深度学习的力量与衍射网络的空间和光谱处理能力相结合，还可以扩展到其他光谱域测量系统，以实现新的3D成像和传感模式，并通过衍射光学网络执行光谱编码分类任务。
英文摘要：
3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction. Here, we demonstrate the design of optical networks in the form of multiple diffractive layers that are trained using deep learning to transform and encode the spatial information of objects into the power spectrum of the diffracted light, which are used to perform optical classification of objects with a single-pixel spectroscopic detector. Using a time-domain spectroscopy setup with a plasmonic nanoantenna-based detector, we experimentally validated this machine vision framework at terahertz spectrum to optically classify the images of handwritten digits by detecting the spectral power of the diffracted light at ten distinct wavelengths, each representing one class/digit. We also report the coupling of this spectral encoding achieved through a diffractive optical network with a shallow electronic neural network, separately trained to reconstruct the images of handwritten digits based on solely the spectral information encoded in these ten distinct wavelengths within the diffracted light. These reconstructed images demonstrate task-specific image decompression and can also be cycled back as new inputs to the same diffractive network to improve its optical object classification. This unique machine vision framework merges the power of deep learning with the spatial and spectral processing capabilities of diffractive networks, and can also be extended to other spectral-domain measurement systems to enable new 3D imaging and sensing modalities integrated with spectrally encoded classification tasks performed through diffractive optical networks.
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