Spinning Metasurface Stack for Spectro-polarimetric Thermal Imaging

TL;DR

This paper presents a compact, robust metasurface stack for LWIR spectro-polarimetric thermal imaging, combining optical design and computational algorithms to achieve high spectral and polarimetric resolution without bulky components.

Contribution

It introduces a novel co-designed metasurface stack and computational approach for thermal spectro-polarimetric imaging, enabling compact, high-resolution, wide FOV thermal sensors.

Findings

Enhanced spectral resolving power improves material classification accuracy.

The metasurface stack is compact (<10x10x10 cm) and has a wide 20.5° FOV.

The system outperforms conventional thermal cameras in machine learning tasks.

Abstract

Spectro-polarimetric imaging in the long-wave infrared (LWIR) region plays a crucial role in applications from night vision and machine perception to trace gas sensing and thermography. However, the current generation of spectro-polarimetric LWIR imagers suffer from limitations in size, spectral resolution and field of view (FOV). While meta-optics-based strategies for spectro-polarimetric imaging have been explored in the visible spectrum, their potential for thermal imaging remains largely unexplored. In this work, we introduce a novel approach for spectro-polarimetric decomposition by combining large-area stacked meta-optical devices with advanced computational imaging algorithms. The co-design of a stack of spinning dispersive metasurfaces along with compressed sensing and dictionary learning algorithms allows simultaneous spectral and polarimetric resolution without the need for bulky filter wheels or interferometers. Our spinning-metasurface-based spectro polarimetric stack is compact (< 10 x 10 x 10 cm), robust, and offers a wide field of view (20.5{\deg}). We show that the spectral resolving power of our system substantially enhances performance in machine learning tasks such as material classification, a challenge for conventional panchromatic thermal cameras. Our approach represents a significant advance in the field of thermal imaging for a wide range of applications including heat-assisted detection and ranging (HADAR).