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Miniature computational spectral detection technology based on correlation value selection
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摘要:
得益于体积小、结构紧凑、易集成等优势，基于超构表面的微型光谱探测技术近年来被广泛研究。然而，现有基于超构表面的微型光谱探测系统设计过程中，通常缺乏对超构表面透射光谱相关性均值与重建质量的定量分析。现有设计过程中采用随机选择方法，无法保证重建质量最优。本文定量分析了超构表面透射光谱的相关性均值与重建质量的关系，提出了一种用于微型光谱探测的超构表面设计方法。此外，本文还验证了基于超构表面的微型光谱探测技术的光谱特性，相较于随机选择设计方法，本文所提出方法可提高宽带光谱和图像光谱的重建质量。
Abstract:Benefiting from the advantages of small size, compact structure, and easy integration, miniature spectral detection technologies based on metasurfaces have been widely studied in recent years. However, the existing designs of the metasurfaces-based miniature spectral detection system usually lack the quantitative analysis of the relationship between the average correlation values of the metasurfaces transmission spectra and the reconstruction quality. The random selection method used in the existing design process cannot guarantee the optimal reconstruction quality. This paper quantitatively analyzes the relationship between the average correlation value of the metasurfaces transmission spectra and reconstruction quality, and proposes a design methodology for miniature spectral detection based on metasurfaces. In addition, this paper also verifies the spectral properties of the metasurfaces-based miniature spectral detection technology. Compared with the random selection design methodology, the proposed methodology can improve the reconstruction fidelity of broadband spectral and image signals.
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Key words:
- quantitative analysis /
- metasurfaces /
- methodology /
- miniature spectral detection
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Overview: Spectral imaging detection technology has been widely used in many fields, such as remote sensing, medical diagnosis, food safety testing, environmental monitoring, and other fields due to its advantages of accurate and non-contact detection. However, conventional spectral imaging systems usually suffer from the large volume, long sampling time, and low energy efficiency. Metasurface is an artificial two-dimensional material that can flexibly control the amplitude, phase and spectrum of electromagnetic waves. Metasurfaces have been used in spectral detection, holography, metalens, and other fields due to its compact structure and the capacity to flexibly control the electromagnetic waves. Benefiting from the advantages of small size, compact structure, and easy integration, miniature spectral detection technologies based on metasurfaces have been widely studied in recent years. The miniature spectral detection systems usually utilize the broadband spectral properties of metasurfaces and compressive sensing algorithms to achieve computational spectral imaging detection with lightweight. However, the existing designs of the metasurfaces-based miniature spectral detection system usually lack the quantitative analysis of the relationship between the average correlation values of the metasurfaces transmission spectra and the reconstruction quality. The random selection method used in the existing design process cannot guarantee the optimal reconstruction quality. Different from the traditional methodology of using the maximum linear independence criterion to select the broadband filters, this paper quantitatively analyzes the relationship between the average correlation value of the metasurfaces transmission spectra and reconstruction quality, and proposes a methodology for miniature spectral detection based on metasurfaces, which provides a route for the subsequent design and optimization of the metasurfaces. In order to verify the advantages of the proposed methodology, ten broadband spectra and image spectra were selected from many spectra. Compared with the random selection design methodology, the proposed methodology can improve the reconstruction fidelity of broadband spectral and image signals. The fidelity of the broadband spectral reconstruction can be increased by 13.17%, and the reconstruction fidelity of the image spectral signals has also been improved to a certain extent. In addition, this paper also verifies the spectral properties of the metasurfaces-based miniature spectral detection technology, showing that the system has good reconstruction effect for broadband, narrowband and image spectral signals, and has the advantages of compact structure and small volume.
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图 1 微型光谱探测。(a) 原理示意图；(b) 多个微型光谱仪的示意图；(c) 单个微型光谱仪及超构表面透射光谱的示意图
Figure 1. Miniature spectral detection. (a) Schematic diagram of the working principle; (b) Schematic diagram of numerous micro-spectrometers; (c) Schematic diagram of a single micro-spectrometer and transmission spectrum of a metasurface
图 2 超构表面的设计。(a) 超构表面的单元结构；(b) 单个微型光谱仪的示意图；(c) 根据不同相关性均值间隔对超构表面进行选择的示意图；(d) 不同图样的超构表面透射光谱
Figure 2. Design of the metasurfaces. (a) The unit cell of the metasurfaces; (b) Schematic diagram of a single micro-spectrometer; (c) Schematic diagram of the selection of metasurfaces according to different average correlation value intervals; (d) Transmission spectra of different patterns of the metasurfaces
图 3 本文所提出方法与传统方法的流程图
Figure 3. Flow chart of our proposed methodology and traditional methodology
图 4 表1中不同超构表面选择设计方法所产生的重建保真度。(a)表1中的光谱1~5；(b)表1中的光谱6~10；(c) 在光谱5下，采用不同的超构表面设计方法所产生的重建保真度；(d) 在光谱10下，采用不同的超构表面设计方法所产生的重建保真度
Figure 4. The reconstruction fidelity produced by different metasurfaces selection design methodologies in Table 1. (a) Spectrum 1~5 in Table 1; (b) Spectrum 6~10 in Table 1; (c) The reconstruction fidelity produced by different metasurfaces selection design methodologies under spectrum5; (d) The reconstruction fidelity produced by different metasurfaces selection design methodologies under spectrum10
图 5 光谱特性仿真验证。(a) 中心波长为560 nm，带宽为1.8 nm的入射光谱和重建光谱；(b)图5(a)中心波长处的放大图像；(c) 中心波长间隔为2 nm的光谱分辨率仿真验证；(d) 中心波长间隔为3 nm的光谱分辨率仿真验证；(e) 不同结构数量M下，宽带光谱1的重建光谱及重建保真度；(f) 不同结构数量M下，宽带光谱2的重建光谱及重建保真度
Figure 5. Spectral characteristic simulation verification. (a) Incident spectrum and the reconstructed spectrum with a central wavelength of 560 nm and a bandwidth of 1.8 nm; (b) Enlarged images around the central wavelength in Fig. 5(a); (c) Spectral resolution simulation verification with a central wavelength interval of 2 nm; (d) Spectral resolution simulation verification with a central wavelength interval of 3 nm; (e) Reconstruction spectrum and reconstruction fidelity of broadband spectrum 1 under different number of structures M; (f) Reconstruction spectrum and reconstruction fidelity of broadband spectrum 2 under different number of structures M
图 6 图像光谱信号感知验证。(a) 原始的图像光谱信号[46]；(b) 重建的图像光谱信号； (c) 在不同色块下，两种超构表面设计方法所产生的光谱信号重建保真度。其中重建光谱1由按照相关性均值[0.1~0.3]所选择出的超构表面结构所产生，重建光谱2由按随机选择出的超构表面结构所产生
Figure 6. Image spectral signals perception verification. (a), (b) Original and reconstructed image spectral signals respectively[46]; (c) Reconstruction fidelity of spectral signals generated by different metasurface design methods under different color blocks. The reconstructed spectrum 1 is produced from the metasurface structures selected using the average correlation value interval [0.1~0.3], and the reconstructed spectrum 2 is produced from the randomly selected metasurface structures
表 1 不同超构表面选择设计方法所产生的重建保真度
Table 1. The reconstruction fidelity produced by different metasurfaces selection design methodologies
光谱 本文提出方法所产生(处于不同相关性均值间隔)的信号重建保真度 传统随机选择方法所产生的
信号重建保真度/%本文方法所产生的信号
重建保真度增幅/%[0.1~0.3]/% [0.3~0.5]/% [0.5~0.7]/% [0.7~0.9]/% 光谱1 92.36 91.58 86.99 69.18 89.20 3.50 光谱2 93.83 88.70 87.55 71.95 89.77 4.52 光谱3 97.62 53.01 48.64 50.33 90.84 7.46 光谱4 98.52 77.91 75.09 71.65 90.20 9.22 光谱5 97.07 82.74 79.10 60.06 87.17 11.36 光谱6 96.41 92.29 90.05 85.01 89.32 7.94 光谱7 96.58 94.40 94.48 85.60 91.83 5.17 光谱8 95.75 89.02 90.34 63.26 87.13 9.89 光谱9 98.64 88.05 91.05 83.88 93.96 3.98 光谱10 97.54 94.10 82.70 42.34 86.65 13.17 
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[1] Shaw G A, Burke H H K. Spectral imaging for remote sensing[J]. Lincoln Lab J, 2003, 14(1): 3−28.
[2] Greaves J S, Richards A M S, Bains W, et al. Phosphine gas in the cloud decks of Venus[J]. Nat Astron, 2021, 5(7): 655−664. doi: 10.1038/s41550-020-1174-4
[3] Bahauddin S M, Bradshaw S J, Winebarger A R. The origin of reconnection-mediated transient brightenings in the solar transition region[J]. Nat Astron, 2021, 5(3): 237−245. doi: 10.1038/s41550-020-01263-2
[4] Vilaseca M, Mercadal R, Pujol J, et al. Characterization of the human iris spectral reflectance with a multispectral imaging system[J]. Appl Opt, 2008, 47(30): 5622−5630. doi: 10.1364/AO.47.005622
[5] Panasyuk S V, Yang S, Faller D V, et al. Medical hyperspectral imaging to facilitate residual tumor identification during surgery[J]. Cancer Biol Ther, 2007, 6(3): 439−446. doi: 10.4161/cbt.6.3.4018
[6] Askoura M L, Vaudelle F, L'Huillier J P. Multispectral measurement of scattering-angular light distribution in apple skin and flesh samples[J]. Appl Opt, 2016, 55(32): 9217−9225. doi: 10.1364/AO.55.009217
[7] Davis C O. Applications of hyperspectral imaging in the coastal ocean[J]. Proc SPIE, 2002, 4816: 33−41. doi: 10.1117/12.453791
[8] Cao X, Yue T, Lin X, et al. Computational snapshot multispectral cameras: toward dynamic capture of the spectral world[J]. IEEE Signal Proc Mag, 2016, 33(5): 95−108. doi: 10.1109/MSP.2016.2582378
[9] Wagadarikar A, John R, Willett R, et al. Single disperser design for coded aperture snapshot spectral imaging[J]. Appl Opt, 2008, 47(10): B44−B51. doi: 10.1364/AO.47.000B44
[10] Gehm M E, John R, Brady D J, et al. Single-shot compressive spectral imaging with a dual-disperser architecture[J]. Opt Express, 2007, 15(21): 14013−14027. doi: 10.1364/OE.15.014013
[11] Wagadarikar A A, Pitsianis N P, Sun X B, et al. Spectral image estimation for coded aperture snapshot spectral imagers[J]. Proc SPIE, 2008, 7076: 707602. doi: 10.1117/12.795545
[12] Ma X L, Pu M B, Li X, et al. All-metallic wide-angle metasurfaces for multifunctional polarization manipulation[J]. Opto-Electron Adv, 2019, 2(3): 180023. doi: 10.29026/oea.2019.180023
[13] Zhang Y B, Liu H, Cheng H, et al. Multidimensional manipulation of wave fields based on artificial microstructures[J]. Opto-Electron Adv, 2020, 3(11): 200002. doi: 10.29026/oea.2020.200002
[14] Yue Z, Li J T, Li J, et al. Terahertz metasurface zone plates with arbitrary polarizations to a fixed polarization conversion[J]. Opto-Electron Sci, 2022, 1(3): 210014. doi: 10.29026/oes.2022.210014
[15] Gao H, Fan X H, Xiong W, et al. Recent advances in optical dynamic meta-holography[J]. Opto-Electron Adv, 2021, 4(11): 210030. doi: 10.29026/oea.2021.210030
[16] Larouche S, Tsai Y J, Tyler T, et al. Infrared metamaterial phase holograms[J]. Nat Mater, 2012, 11(5): 450−454. doi: 10.1038/nmat3278
[17] Zhang F, Pu M B, Gao P, et al. Simultaneous full-color printing and holography enabled by centimeter-scale plasmonic metasurfaces[J]. Adv Sci, 2020, 7(10): 1903156. doi: 10.1002/advs.201903156
[18] Faraji-Dana M S, Arbabi E, Arbabi A, et al. Compact folded metasurface spectrometer[J]. Nat Commun, 2018, 9(1): 4196. doi: 10.1038/s41467-018-06495-5
[19] Horie Y, Arbabi A, Arbabi E, et al. Wide bandwidth and high resolution planar filter array based on DBR-metasurface-DBR structures[J]. Opt Express, 2016, 24(11): 11677−11682. doi: 10.1364/OE.24.011677
[20] Redding B, Liew S F, Sarma R, et al. Compact spectrometer based on a disordered photonic chip[J]. Nat Photonics, 2013, 7(9): 746−751. doi: 10.1038/nphoton.2013.190
[21] Dou K H, Xie X, Pu M B, et al. Off-axis multi-wavelength dispersion controlling metalens for multi-color imaging[J]. Opto-Electron Adv, 2020, 3(4): 190005. doi: 10.29026/oea.2020.190005
[22] Khorasaninejad M, Chen W T, Devlin R C, et al. Metalenses at visible wavelengths: diffraction-limited focusing and subwavelength resolution imaging[J]. Science, 2016, 352(6290): 1190−1194. doi: 10.1126/science.aaf6644
[23] Wang Y L, Fan Q B, Xu T. Design of high efficiency achromatic metalens with large operation bandwidth using bilayer architecture[J]. Opto-Electron Adv, 2021, 4(1): 200008. doi: 10.29026/oea.2021.200008
[24] Wang H T, Hao C L, Lin H, et al. Generation of super-resolved optical needle and multifocal array using graphene oxide metalenses[J]. Opto-Electron Adv, 2021, 4(2): 200031. doi: 10.29026/oea.2021.200031
[25] 周毅, 梁高峰, 温中泉, 等. 光学超分辨平面超构透镜研究进展[J]. 光电工程, 2021, 48(12): 210399. doi: 10.12086/oee.2021.210399
Zhou Y, Liang G F, Wen Z Q, et al. Recent research progress in optical super-resolution planar meta-lenses[J]. Opto-Electron Eng, 2021, 48(12): 210399. doi: 10.12086/oee.2021.210399
[26] 申益佳, 谢鑫, 蒲明博, 等. 基于传输相位和几何相位协同调控的消色差超透镜[J]. 光电工程, 2020, 47(10): 200237. doi: 10.12086/oee.2020.200237
Shen Y J, Xie X, Pu M B, et al. Achromatic metalens based on coordinative modulation of propagation phase and geometric phase[J]. Opto-Electron Eng, 2020, 47(10): 200237. doi: 10.12086/oee.2020.200237
[27] Fang N, Lee H, Sun C, et al. Sub-diffraction-limited optical imaging with a silver superlens[J]. Science, 2005, 308(5721): 534−537. doi: 10.1126/science.1108759
[28] Luo X G, Ishihara T. Surface plasmon resonant interference nanolithography technique[J]. Appl Phys Lett, 2004, 84(23): 4780−4782. doi: 10.1063/1.1760221
[29] Butt H, Montelongo Y, Butler T, et al. Carbon nanotube based high resolution holograms[J]. Adv Mater, 2012, 24(44): OP331−OP336. doi: 10.1002/adma.201202593
[30] Wang Z, Yi S, Chen A, et al. Single-shot on-chip spectral sensors based on photonic crystal slabs[J]. Nat Commun, 2019, 10(1): 1020. doi: 10.1038/s41467-019-08994-5
[31] Xiong J, Cai X S, Cui K Y, et al. Dynamic brain spectrum acquired by a real-time ultraspectral imaging chip with reconfigurable metasurfaces[J]. Optica, 2022, 9(5): 461−468. doi: 10.1364/OPTICA.440013
[32] Bao J, Bawendi M G. A colloidal quantum dot spectrometer[J]. Nature, 2015, 523(7558): 67−70. doi: 10.1038/nature14576
[33] Zhu Y B, Lei X, Wang K X, et al. Compact CMOS spectral sensor for the visible spectrum[J]. Photonics Res, 2019, 7(9): 961−966. doi: 10.1364/PRJ.7.000961
[34] Zhang W Y, Song H Y, He X, et al. Deeply learned broadband encoding stochastic hyperspectral imaging[J]. Light Sci Appl, 2021, 10(1): 108. doi: 10.1038/s41377-021-00545-2
[35] Yang Z Y, Albrow-Owen T, Cui H X, et al. Single-nanowire spectrometers[J]. Science, 2019, 365(6457): 1017−1020. doi: 10.1126/science.aax8814
[36] Meng J J, Cadusch J J, Crozier K B. Detector-only spectrometer based on structurally colored silicon nanowires and a reconstruction algorithm[J]. Nano Lett, 2020, 20(1): 320−328. doi: 10.1021/acs.nanolett.9b03862
[37] Kwak Y, Park S M, Ku Z, et al. A pearl spectrometer[J]. Nano Lett, 2021, 21(2): 921−930. doi: 10.1021/acs.nanolett.0c03618
[38] 李遂贤. 基于多目标优化的多光谱相机的宽带滤色片选取[J]. 光学学报, 2020, 40(4): 0411001. doi: 10.3788/AOS202040.0411001
Li S X. Broadband filter selection for multispectral camera based on multi-objective optimization[J]. Acta Opt Sin, 2020, 40(4): 0411001. doi: 10.3788/AOS202040.0411001
[39] Li S X. Filter selection for optimizing the spectral sensitivity of broadband multispectral cameras based on maximum linear independence[J]. Sensors, 2018, 18(5): 1455. doi: 10.3390/s18051455
[40] Li S X, Zhang L Y. Optimal sensitivity design of multispectral camera via broadband absorption filters based on compressed sensing[C]//3rd International Symposium of Space Optical Instruments and Applications, 2017: 329–339. doi: 10.1007/978-3-319-49184-4_33.
[41] Li S X. Superiority of optimal broadband filter sets under lower noise levels in multispectral color imaging[J]. Color Res Appl, 2021, 46(4): 783−790. doi: 10.1002/col.22630
[42] Zhang S, Dong Y H, Fu H Y, et al. A spectral reconstruction algorithm of miniature spectrometer based on sparse optimization and dictionary learning[J]. Sensors, 2018, 18(2): 644. doi: 10.3390/s18020644
[43] Donoho D L. Compressed sensing[J]. IEEE Trans Inf Theory, 2006, 52(4): 1289−1306. doi: 10.1109/TIT.2006.871582
[44] Baraniuk R. Compressive sensing[C]//42nd Annual Conference on Information Sciences and Systems, 2008. doi: 10.1109/CISS.2008.4558479.
[45] Figueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems[J]. IEEE J Sel Top Signal Proc, 2007, 1(4): 586−597. doi: 10.1109/JSTSP.2007.910281
[46] Arad B, Ben-Shahar O. Sparse recovery of hyperspectral signal from natural RGB images[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 19–34. doi: 10.1007/978-3-319-46478-7_2.
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