Spinning Metasurface Stack for Spectro-polarimetric Thermal Imaging
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866910295612456960 |
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| author | Wang, Xueji Yang, Ziyi Bao, Fanglin Sentz, Tyler Jacob, Zubin |
| author_facet | Wang, Xueji Yang, Ziyi Bao, Fanglin Sentz, Tyler Jacob, Zubin |
| contents | 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°). 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_11519 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Spinning Metasurface Stack for Spectro-polarimetric Thermal Imaging Wang, Xueji Yang, Ziyi Bao, Fanglin Sentz, Tyler Jacob, Zubin Optics Applied Physics 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°). 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). |
| title | Spinning Metasurface Stack for Spectro-polarimetric Thermal Imaging |
| topic | Optics Applied Physics |
| url | https://arxiv.org/abs/2301.11519 |