用超表面+新算法实现紧凑型红外测温，抗色差且无需大量训练数据
End-to-end metasurface design for temperature imaging via broadband Planck-radiation regression
- 端到端优化单层超表面与非线性回归算法联合设计
- 在8-12μm波段实现任意温度图的高保真重建，抗噪性强
- 比神经网络更通用，无需特定场景训练数据
我们提出一种理论框架，通过端到端设计超表面光学前端与计算重建后端，实现从长波红外热辐射（如8-12 μm）中进行温度成像。引入一种新型非线性重建算法「Planck regression」，利用黑体辐射与光学物理特性，在存在严重色差的情况下，仍能从灰度传感器图像重建温度分布。该算法与端到端方法结合，优化可制造的单层超表面，实现最高精度重建。仿真结果表明，该设计可在超紧凑热成像设备中高质量、鲁棒地还原任意温度图（包括完全随机图像）。此外，Planck regression对任意图像的泛化能力远超传统神经网络重建，后者需大量领域特定图像训练集。
原文摘要 · Abstract (English)
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.
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