Bai Bijie, Li Yuhang, Luo Yi, Li Xurong, Çetintaş Ege, Jarrahi Mona, Ozcan Aydogan
Electrical and Computer Engineering Department, University of California, Los Angeles, California, 90095, USA.
Bioengineering Department, University of California, Los Angeles, California, 90095, USA.
Light Sci Appl. 2023 Mar 9;12(1):69. doi: 10.1038/s41377-023-01116-3.
Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields. Recent deep learning-based approaches demonstrated the classification of objects using diffuser-distorted patterns collected by an image sensor. These methods demand relatively large-scale computing using deep neural networks running on digital computers. Here, we present an all-optical processor to directly classify unknown objects through unknown, random phase diffusers using broadband illumination detected with a single pixel. A set of transmissive diffractive layers, optimized using deep learning, forms a physical network that all-optically maps the spatial information of an input object behind a random diffuser into the power spectrum of the output light detected through a single pixel at the output plane of the diffractive network. We numerically demonstrated the accuracy of this framework using broadband radiation to classify unknown handwritten digits through random new diffusers, never used during the training phase, and achieved a blind testing accuracy of 87.74 ± 1.12%. We also experimentally validated our single-pixel broadband diffractive network by classifying handwritten digits "0" and "1" through a random diffuser using terahertz waves and a 3D-printed diffractive network. This single-pixel all-optical object classification system through random diffusers is based on passive diffractive layers that process broadband input light and can operate at any part of the electromagnetic spectrum by simply scaling the diffractive features proportional to the wavelength range of interest. These results have various potential applications in, e.g., biomedical imaging, security, robotics, and autonomous driving.
对位于随机且未知散射介质后的物体进行分类，给计算成像和机器视觉领域带来了一项具有挑战性的任务。最近基于深度学习的方法展示了利用图像传感器收集的经扩散器扭曲的图案对物体进行分类。这些方法需要使用在数字计算机上运行的深度神经网络进行相对大规模的计算。在此，我们提出一种全光处理器，通过使用单像素检测的宽带照明，直接对穿过未知随机相位扩散器的未知物体进行分类。一组使用深度学习优化的透射衍射层形成一个物理网络，该网络将随机扩散器后输入物体的空间信息全光映射到通过衍射网络输出平面上的单像素检测到的输出光的功率谱中。我们通过使用宽带辐射对从未在训练阶段使用过的随机新扩散器后的未知手写数字进行分类，在数值上证明了该框架的准确性，并实现了87.74±1.12%的盲测准确率。我们还通过使用太赫兹波和3D打印衍射网络对穿过随机扩散器的手写数字“0”和“1”进行分类，对我们的单像素宽带衍射网络进行了实验验证。这种通过随机扩散器的单像素全光物体分类系统基于处理宽带输入光的无源衍射层，并且通过简单地按与感兴趣的波长范围成比例缩放衍射特征，可在电磁频谱的任何部分运行。这些结果在例如生物医学成像、安全、机器人技术和自动驾驶等方面有各种潜在应用。
