Optical corner detection with azimuthal Hilbert transform metasurfaces.

Abstract

Many animals efficiently interpret their environment by detecting geometric features like corners, highlighting the power of feature extraction for reducing visual complexity; similarly, with the surge in visual data, nature-inspired optical corner detection offers a promising yet still elusive solution for energy-efficient information processing and compression. Here, we propose a universal strategy for optical corner imaging with azimuthal Hilbert transformation metasurfaces. Multiple objects, regardless of their amplitude, phase, or angular characteristics, can be detected simultaneously with a single metasurface, featuring broadband and full-field-of-view properties. Trade-offs between spatial and angular resolution are assessed, offering practical guidance for implementation. We further demonstrate motion tracking as a proof-of-concept application leveraging the data-compressed corner imaging framework. This work paves the way for next-generation optical information processing technologies.