近日，芬兰坦佩雷大学Humeyra Caglayan团队研究了用于全光学图像处理的双相超表面算子。2026年2月23日出版的《光：科学与应用》杂志发表了这项成果。

全光图像处理通过利用光的波动性进行并行计算，为传统电子系统提供了高速、节能的替代方案。然而，传统光学处理器依赖笨重的分立元件，限制了其可扩展性和集成度。

研究组展示了一种基于超表面的紧凑型模拟光学计算平台。通过采用双相位编码和偏振复用技术，该方法可在单个无源纳米光子器件中实现任意图像变换，无需复杂的光学设置或数字后处理。研究组实验验证了关键计算操作，包括一阶微分、互相关、顶点检测和拉普拉斯微分。

此外，研究组将该框架扩展到高分辨率复杂全息领域，实现了亚波长尺度的体波前调控，用于高保真度的深度分辨重建。该研究成果为计算光学建立了一种可扩展且多功能的方法，其应用包括实时图像处理、节能计算、生物医学成像、高保真全息显示和光学数据存储，推动智能光学处理器的发展。

附：英文原文

Title: Double-phase metasurface operators for all-optical image processing

Author: Yu, Linzhi, Singh, Haobijam J., Pietila, Jesse, Caglayan, Humeyra

Issue&Volume: 2026-02-23

Abstract: All-optical image processing offers a high-speed, energy-efficient alternative to conventional electronic systems by leveraging the wave nature of light for parallel computation. However, traditional optical processors rely on bulky components, limiting scalability and integration. Here, we demonstrate a compact metasurface-based platform for analog optical computing. By employing double-phase encoding and polarization multiplexing, our approach enables arbitrary image transformations within a single passive nanophotonic device, eliminating the need for complex optical setups or digital post-processing. We experimentally showcase key computational operations, including first-order differentiation, cross-correlation, vertex detection, and Laplacian differentiation. Additionally, we extend this framework to high-resolution complex holography, achieving subwavelength-scale volumetric wavefront control for depth-resolved reconstructions with high fidelity. Our results establish a scalable and versatile approach to computational optics, with applications including real-time image processing, energy-efficient computing, biomedical imaging, high-fidelity holographic displays, and optical data storage, driving the advancement of intelligent optical processors.

DOI: 10.1038/s41377-025-02153-w

Source: https://www.nature.com/articles/s41377-025-02153-w