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通过编码超表面实现的实时机器学习增强型高光谱偏振成像。

Real-time machine learning-enhanced hyperspectro-polarimetric imaging via an encoding metasurface.

作者信息

Zhang Lidan, Zhou Chen, Liu Bofeng, Ding Yimin, Ahn Hyun-Ju, Chang Shengyuan, Duan Yao, Rahman Md Tarek, Xia Tunan, Chen Xi, Liu Zhiwen, Ni Xingjie

机构信息

Department of Electrical Engineering, the Pennsylvania State University, University Park, PA 16802, United States.

出版信息

Sci Adv. 2024 Sep 6;10(36):eadp5192. doi: 10.1126/sciadv.adp5192. Epub 2024 Sep 4.

Abstract

Light fields carry a wealth of information, including intensity, spectrum, and polarization. However, standard cameras capture only the intensity, disregarding other valuable information. While hyperspectral and polarimetric imaging systems capture spectral and polarization information, respectively, in addition to intensity, they are often bulky, slow, and costly. Here, we have developed an encoding metasurface paired with a neural network enabling a normal camera to acquire hyperspectro-polarimetric images from a single snapshot. Our experimental results demonstrate that this metasurface-enhanced camera can accurately resolve full-Stokes polarization across a broad spectral range (700 to 1150 nanometer) from a single snapshot, achieving a spectral sensitivity as high as 0.23 nanometer. In addition, our system captures full-Stokes hyperspectro-polarimetric video in real time at a rate of 28 frames per second, primarily limited by the camera's readout rate. Our encoding metasurface offers a compact, fast, and cost-effective solution for multidimensional imaging that effectively uses information within light fields.

摘要

光场携带丰富的信息,包括强度、光谱和偏振。然而,标准相机仅捕捉强度信息,而忽略了其他有价值的信息。虽然高光谱成像系统和偏振成像系统除了强度信息外,分别能捕捉光谱信息和偏振信息,但它们通常体积庞大、速度缓慢且成本高昂。在此,我们开发了一种与神经网络配对的编码超表面,使普通相机能够从单次快照中获取高光谱偏振图像。我们的实验结果表明,这种超表面增强相机能够从单次快照中在宽光谱范围(700至1150纳米)内准确解析全斯托克斯偏振,实现高达0.23纳米的光谱灵敏度。此外,我们的系统以每秒28帧的速率实时捕捉全斯托克斯高光谱偏振视频,主要受相机读出速率的限制。我们的编码超表面为多维成像提供了一种紧凑、快速且经济高效的解决方案,能够有效利用光场内的信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2c/11373597/ca816dd97837/sciadv.adp5192-f1.jpg

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