Sophie Fisher, Gaurav Arya, Arka Majumdar, Zin Lin, Steven G. Johnson
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引用次数: 0
Abstract
A theoretical framework is presented for temperature imaging from long-wavelength infrared (LWIR) thermal radiation (e.g., 8–12 µm) through the end-to-end design of a metasurface-optics frontend and a computational-reconstruction backend. A new nonlinear reconstruction algorithm, “Planck regression”, is introduced to reconstruct the temperature map from a gray scale sensor image, even in the presence of severe chromatic aberration, by exploiting black body and optical physics particular to thermal imaging. This algorithm is combined with an end-to-end approach that optimizes manufacturable, single-layer metasurfaces to yield the most accurate reconstruction. The designs demonstrate high-quality, noise-robust reconstructions of arbitrary temperature maps (including completely random images) in simulations of an ultra-compact thermal-imaging device. It is also shown 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.
期刊介绍:
Advanced Optical Materials, part of the esteemed Advanced portfolio, is a unique materials science journal concentrating on all facets of light-matter interactions. For over a decade, it has been the preferred optical materials journal for significant discoveries in photonics, plasmonics, metamaterials, and more. The Advanced portfolio from Wiley is a collection of globally respected, high-impact journals that disseminate the best science from established and emerging researchers, aiding them in fulfilling their mission and amplifying the reach of their scientific discoveries.