Noise-Adaptive Intelligent Programmable Meta-Imager

TL;DR

This paper introduces a noise-adaptive programmable meta-imager that optimizes scene illumination patterns for specific tasks and noise conditions, improving performance in challenging environments like surveillance and earth observation.

Contribution

It develops an end-to-end differentiable pipeline for jointly designing physical illumination patterns and digital processing, adapting to noise levels for enhanced imaging performance.

Findings

Outperforms pseudo-random illumination under high noise and latency constraints

Analyzes learned illumination patterns and their dependence on noise

Uses an analytical coupled-dipole model for design and analysis

Abstract

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.