# End-to-end metasurface design for temperature imaging via broadband Planck-radiation regression

arXiv: https://arxiv.org/abs/2409.08456 · HTML: https://mlsanity.com/paper/2409.08456/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout](https://mlsanity.com/paper/2606.09792/) (2026)

## Abstract (arXiv)

We present a theoretical framework for temperature imaging from long-wavelength infrared thermal radiation (e.g. 8-12 μm) 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.