Noise-Adaptive Intelligent Programmable Meta-Imager

• Main Authors: Qian, Chenqi, del Hougne, Philipp
• Format: Journal Article
• Language: English
• Published: 22-08-2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Information Theory; Mathematics - Information Theory; Physics - Applied Physics; Physics - Optics
• DOI: 10.48550/arxiv.2208.10171

Intell. Comput. 2022, 9825738 (2022) We present an intelligent programmable computational meta-imager that tailors
its sequence of coherent scene illuminations not only to a specific
information-extraction task (e.g., object recognition) but also adapts to
different types and levels of noise. We systematically study how the learned
illumination patterns depend on the noise, and we discover that trends in
intensity and overlap of the learned illumination patterns can be understood
intuitively. We conduct our analysis based on an analytical coupled-dipole
forward model of a microwave dynamic metasurface antenna (DMA); we formulate a
differentiable end-to-end information-flow pipeline comprising the programmable
physical measurement process including noise as well as the subsequent digital
processing layers. This pipeline allows us to jointly inverse-design the
programmable physical weights (DMA configurations that determine the coherent
scene illuminations) and the trainable digital weights. Our noise-adaptive
intelligent meta-imager outperforms the conventional use of pseudo-random
illumination patterns most clearly under conditions that make the extraction of
sufficient task-relevant information challenging: latency constraints (limiting
the number of allowed measurements) and strong noise. Programmable microwave
meta-imagers in indoor surveillance and earth observation will be confronted
with these conditions.