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Conventional imagers rely on power\u2010intensive digital postprocessing for edge detection, creating a data bottleneck between the image sensor and signal processor. We propose a system\u2010level hybrid optoelectronic edge imaging framework that combines an analog optical computing metalens with a compressive sensing CMOS image sensor (CS\u2010CIS). The spiral metalens with a topological charge of  directly images and encodes second\u2010order 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\u2010CIS that selects a reduced number of pixel values, minimizing redundant data acquisition and enabling high\u2010speed and ultra\u2010low\u2010energy operation. Simulation results validate the effectiveness of this approach, demonstrating a peak signal\u2010to\u2010noise ratio of 23.87\u00a0dB at a 25.0% compression rate. This work paves the way for energy\u2010efficient edge\u2010aware systems with potential applications in robotics and computer vision.<\/jats:p>","DOI":"10.1002\/nap2.70079","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T10:07:27Z","timestamp":1776334047000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid Metalens\u2013Compressive Sensing Imaging System for Ultra\u2010Low\u2010Power Edge Detection"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4972-1018","authenticated-orcid":false,"given":"Sejin","family":"Yoon","sequence":"first","affiliation":[{"name":"Department of Electronics and Information Engineering Korea University  Sejong South Korea"},{"name":"Department of AI Semiconductor Engineering Korea University  Sejong South 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