End-to-end metasurface design for temperature imaging via broadband Planck-radiation regression
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arXiv
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| Format: | Preprint |
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2024
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| author | Fisher, Sophie Arya, Gaurav Majumdar, Arka Lin, Zin Johnson, Steven G. |
| author_facet | Fisher, Sophie Arya, Gaurav Majumdar, Arka Lin, Zin Johnson, Steven G. |
| contents | We present a theoretical framework for temperature imaging from long-wavelength infrared thermal radiation (e.g. 8-12 $μ$m) through the end-to-end design of a metasurface-optics frontend and a computational-reconstruction backend. We introduce a new nonlinear reconstruction algorithm, ``Planck regression," that reconstructs the temperature map from a grayscale sensor image, even in the presence of severe chromatic aberration, by exploiting blackbody and optical physics particular to thermal imaging. We combine this algorithm with an end-to-end approach that optimizes a manufacturable, single-layer metasurface to yield the most accurate reconstruction. Our designs demonstrate high-quality, noise-robust reconstructions of arbitrary temperature maps (including completely random images) in simulations of an ultra-compact thermal-imaging device. We also show that Planck regression is much more generalizable to arbitrary images than a straightforward neural-network reconstruction, which requires a large training set of domain-specific images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08456 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | End-to-end metasurface design for temperature imaging via broadband Planck-radiation regression Fisher, Sophie Arya, Gaurav Majumdar, Arka Lin, Zin Johnson, Steven G. Optics Image and Video Processing Optimization and Control We present a theoretical framework for temperature imaging from long-wavelength infrared thermal radiation (e.g. 8-12 $μ$m) through the end-to-end design of a metasurface-optics frontend and a computational-reconstruction backend. We introduce a new nonlinear reconstruction algorithm, ``Planck regression," that reconstructs the temperature map from a grayscale sensor image, even in the presence of severe chromatic aberration, by exploiting blackbody and optical physics particular to thermal imaging. We combine this algorithm with an end-to-end approach that optimizes a manufacturable, single-layer metasurface to yield the most accurate reconstruction. Our designs demonstrate high-quality, noise-robust reconstructions of arbitrary temperature maps (including completely random images) in simulations of an ultra-compact thermal-imaging device. We also show that Planck regression is much more generalizable to arbitrary images than a straightforward neural-network reconstruction, which requires a large training set of domain-specific images. |
| title | End-to-end metasurface design for temperature imaging via broadband Planck-radiation regression |
| topic | Optics Image and Video Processing Optimization and Control |
| url | https://arxiv.org/abs/2409.08456 |