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.
Chen et al. (Wed,) studied this question.