Hybrid Metalens–Compressive Sensing Imaging System for Ultra‐Low‐Power Edge Detection

ABSTRACT Low‐power edge detection is essential for next‐generation vision systems that require real‐time on‐device processing of optical information. Conventional imagers rely on power‐intensive digital postprocessing for edge detection, creating a data bottleneck between the image sensor and signal processor. We propose a system‐level hybrid optoelectronic edge imaging framework that combines an analog optical computing metalens with a compressive sensing CMOS image sensor (CS‐CIS). The spiral metalens with a topological charge of directly images and encodes second‐order edge information in the optical domain, eliminating the need for additional bulky optics and digital edge extraction algorithms. This optically processed information is captured by the CS‐CIS that selects a reduced number of pixel values, minimizing redundant data acquisition and enabling high‐speed and ultra‐low‐energy operation. Simulation results validate the effectiveness of this approach, demonstrating a peak signal‐to‐noise ratio of 23.87 dB at a 25.0% compression rate. This work paves the way for energy‐efficient edge‐aware systems with potential applications in robotics and computer vision.