End‐to‐End Metasurface Design for Temperature Imaging via Broadband Planck‐Radiation Regression

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.