Rapid developments in machine vision have led to advances in a variety of industries, from medical image analysis to autonomous systems. These achievements, however, typically necessitate digital neural networks with heavy computational requirements, which are limited by high energy consumption and further hinder real-time decision-making when computation resources are not accessible. Here, we demonstrate an intelligent meta-imager that is designed to work in concert with a digital back-end to off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positive and negatively valued convolution operations in a single shot. The meta-imager is employed for object classification, experimentally achieving 98.6% accurate classification of handwritten digits and 88.8% accuracy in classifying fashion images. With compactness, high speed, and low power consumption, this approach could find a wide range of applications in artificial intelligence and machine vision applications.

翻译：机器视觉的快速发展推动了从医学图像分析到自主系统等各行业的进步。然而，这些成就通常需要计算需求庞大的数字神经网络，其受限于高能耗，且在缺乏计算资源时会进一步阻碍实时决策。本文展示了一种智能超构成像器，其设计旨在与数字后端协同工作，将计算密集的卷积运算卸载至高速低功耗的光学系统。在该架构中，超表面同时实现角度和偏振复用，以创建多个信息通道，从而在单次拍摄中完成正值和负值的卷积运算。该超构成像器被用于物体分类，实验实现了98.6%的手写数字准确分类和88.8%的时尚图像分类准确率。凭借紧凑性、高速和低功耗特性，该方法可在人工智能和机器视觉应用中具有广泛用途。