Li Jingxi, Mengu Deniz, Yardimci Nezih T, Luo Yi, Li Xurong, Veli Muhammed, Rivenson Yair, Jarrahi Mona, Ozcan Aydogan
Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
Bioengineering Department, University of California, Los Angeles, CA 90095, USA.
Sci Adv. 2021 Mar 26;7(13). doi: 10.1126/sciadv.abd7690. Print 2021 Mar.
We demonstrate optical networks composed of diffractive layers trained using deep learning to encode the spatial information of objects into the power spectrum of the diffracted light, which are used to classify objects with a single-pixel spectroscopic detector. Using a plasmonic nanoantenna-based detector, we experimentally validated this single-pixel 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 coupled this diffractive network-based spectral encoding with a shallow electronic neural network, which was trained to rapidly reconstruct the images of handwritten digits based on solely the spectral power detected at these ten distinct wavelengths, demonstrating task-specific image decompression. This single-pixel machine vision framework can also be extended to other spectral-domain measurement systems to enable new 3D imaging and sensing modalities integrated with diffractive network-based spectral encoding of information.
我们展示了由衍射层组成的光学网络，该网络通过深度学习进行训练，以将物体的空间信息编码到衍射光的功率谱中，并用单像素光谱探测器对物体进行分类。使用基于等离子体纳米天线的探测器，我们在太赫兹光谱下通过实验验证了这个单像素机器视觉框架，通过检测十个不同波长处衍射光的光谱功率来对手写数字图像进行光学分类，每个波长代表一个类别/数字。我们还将这种基于衍射网络的光谱编码与一个浅层电子神经网络相结合，该网络经过训练，仅根据在这十个不同波长处检测到的光谱功率快速重建手写数字图像，展示了特定任务的图像解压缩。这个单像素机器视觉框架还可以扩展到其他光谱域测量系统，以实现与基于衍射网络的信息光谱编码集成的新的三维成像和传感模式。
