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Article Dans Une Revue Applied Physics Reviews Année : 2022

Intelligent meta-imagers: From compressed to learned sensing

Résumé

Computational meta-imagers synergize metamaterial hardware with advanced signal processing approaches such as compressed sensing. Recent advances in artificial intelligence (AI) are gradually reshaping the landscape of meta-imaging. Most recent works use AI for data analysis, but some also use it to program the physical meta-hardware. The role of "intelligence " in the measurement process and its implications for critical metrics like latency are often not immediately clear. Here, we comprehensively review the evolution of computational meta-imaging from the earliest frequency-diverse compressive systems to modern programmable intelligent meta-imagers. We introduce a clear taxonomy in terms of the flow of task-relevant information that has direct links to information theory: compressive meta-imagers indiscriminately acquire all scene information in a task-agnostic measurement process that aims at a near-isometric embedding; intelligent meta-imagers highlight task-relevant information in a task-aware measurement process that is purposefully non-isometric. The measurement process of intelligent meta-imagers is, thus, simultaneously an analog wave processor that implements a first task-specific inference step "over-the-air. " We provide explicit design tutorials for the integration of programmable meta-atoms as trainable physical weights into an intelligent end-to-end sensing pipeline. This merging of the physical world of metamaterial engineering and the digital world of AI enables the remarkable latency gains of intelligent meta-imagers. We further outline emerging opportunities for cognitive meta-imagers with reverberation-enhanced resolution, and we point out how the meta-imaging community can reap recent advances in the vibrant field of metamaterial wave processors to reach the holy grail of low-energy ultra-fast all-analog intelligent meta-sensors.
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hal-03629714 , version 1 (04-04-2022)
hal-03629714 , version 2 (04-04-2022)

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Chloe Saigre-Tardif, Rashid Faqiri, Hanting Zhao, Lianlin Li, Philipp del Hougne. Intelligent meta-imagers: From compressed to learned sensing. Applied Physics Reviews, 2022, 9 (1), pp.011314. ⟨10.1063/5.0076022⟩. ⟨hal-03629714v2⟩
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