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 suffers 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 an 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 compressive 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 ({\lt}\;{10} \times {10} \times {10}\;{\rm cm}${\lt}\;{10} \times {10} \times {10}\;{\rm cm}$) and robust, and it offers a wide field of view (20.5°). 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).

中文翻译：

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用于光谱偏振热成像的旋转超表面堆叠

长波红外 (LWIR) 区域的光谱偏振成像在从夜视和机器感知到痕量气体传感和热成像的应用中发挥着至关重要的作用。然而，当前一代的光谱偏振长波红外成像仪在尺寸、光谱分辨率和视场 (FOV) 方面受到限制。虽然已经在可见光谱中探索了基于元光学的光谱偏振成像策略，但它们在热成像方面的潜力仍然很大程度上未被开发。在这项工作中，我们引入了一种将大面积堆叠元光学设备与先进的计算成像算法相结合的光谱偏振分解方法。一堆旋转色散超表面与压缩传感和字典学习算法的共同设计可以同时实现光谱和偏振分辨率，而不需要笨重的滤光轮或干涉仪。我们的基于旋转超表面的光谱偏振堆栈结构紧凑（{\lt}\;{10} \times {10} \times {10}\;{\rm cm}${\lt}\;{10} \times {10} \times {10}\;{\rm cm}$）且坚固耐用，并且它提供了广泛的视图（20.5°）。我们表明，我们系统的光谱分辨率大大提高了机器学习任务的性能，例如材料分类，这对传统全色热像仪来说是一个挑战。我们的方法代表了热成像领域的重大进步，适用于包括热辅助检测和测距 (HADAR) 在内的广泛应用。