Fu Weiwei, Zhao Dong, Li Ziqin, Liu Songde, Tian Chao, Huang Kun
Department of Optics and Optical Engineering, University of Science and Technology of China, Hefei, Anhui, 230026, China.
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, 230088, China.
Light Sci Appl. 2022 Mar 18;11(1):62. doi: 10.1038/s41377-022-00752-5.
Electronic digital convolutions could extract key features of objects for data processing and information identification in artificial intelligence, but they are time-cost and energy consumption due to the low response of electrons. Although massless photons enable high-speed and low-loss analog convolutions, two existing all-optical approaches including Fourier filtering and Green's function have either limited functionality or bulky volume, thus restricting their applications in smart systems. Here, we report all-optical convolutional computing with a metasurface-singlet or -doublet imager, considered as the third approach, where its point spread function is modified arbitrarily via a complex-amplitude meta-modulator that enables functionality-unlimited kernels. Beyond one- and two-dimensional spatial differentiation, we demonstrate real-time, parallel, and analog convolutional processing of optical and biological specimens with challenging pepper-salt denoising and edge enhancement, which significantly enrich the toolkit of all-optical computing. Such meta-imager approach bridges multi-functionality and high-integration in all-optical convolutions, meanwhile possessing good architecture compatibility with digital convolutional neural networks.
电子数字卷积能够提取物体的关键特征，用于人工智能中的数据处理和信息识别，但由于电子响应速度低，它们存在时间成本和能量消耗问题。尽管无质量的光子能够实现高速、低损耗的模拟卷积，但包括傅里叶滤波和格林函数在内的两种现有的全光方法，要么功能有限，要么体积庞大，从而限制了它们在智能系统中的应用。在此，我们报告了一种使用超表面单重态或双重态成像器的全光卷积计算，这被视为第三种方法，其中通过复振幅元调制器可以任意修改其点扩散函数，从而实现功能不受限的内核。除了一维和二维空间微分之外，我们还展示了对光学和生物样本进行实时、并行和模拟卷积处理，具有具有挑战性的椒盐噪声去除和边缘增强功能，这显著丰富了全光计算的工具集。这种元成像器方法在全光卷积中实现了多功能性和高集成度，同时与数字卷积神经网络具有良好的架构兼容性。
