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CLC number: O433.1

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2020-03-24

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Hongya Song

https://orcid.org/0000-0002-1995-4045

Xiang Hao

https://orcid.org/0000-0002-3931-6884

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.8 P.1119-1133

http://doi.org/10.1631/FITEE.1900266


Review of compact computational spectral information acquisition systems


Author(s):  Hongya Song, Wenyi Zhang, Haifeng Li, Xu Liu, Xiang Hao

Affiliation(s):  State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   haox@zju.edu.cn

Key Words:  Spectral imaging, Computational imaging, Spectrometer


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Hongya Song, Wenyi Zhang, Haifeng Li, Xu Liu, Xiang Hao. Review of compact computational spectral information acquisition systems[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(8): 1119-1133.

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Abstract: 
With the development of computer science, more and more hardware implementations can be reproduced by software programming, bringing compact, cheap, and fast components to imaging instrumentation. In recent years, computational methods have been introduced into spectral detection, and computational spectrum acquisition implementations have emerged. This paper highlights the advantages of computational spectrum acquisition implementations by comparing them with traditional non-computational methods. Then, focusing on the compact feature, we review the most representative implementations, and finally make discussion and offer an outlook.

紧凑型计算光谱信息采集系统综述

宋洪亚1,张文屹1,李海峰1,刘旭1,2,3,郝翔1
1浙江大学光电科学与工程学院现代光学仪器国家重点实验室,中国杭州市,310027
2浙江大学宁波研究院,中国宁波市,315100
3山西大学极端光学协同创新中心,中国太原市,030006

摘要:随着计算机科学的发展,越来越多硬件功能可通过软件编程实现,使得光学仪器更加紧凑、廉价,光学设计和加工也更加方便、快速。近年来,软件算法被引入光谱检测,发展出一些计算型的光谱仪、光谱成像设备等光谱信息采集系统。通过与传统非计算方法比较,本文突出了计算光谱采集的优势。重点关注紧凑性特征,回顾最具代表性的计算光谱信息采集系统,并作讨论和展望。

关键词:光谱成像;计算成像;光谱仪

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Adato R, Yanik AA, Amsden JJ, et al., 2009. Ultra-sensitive vibrational spectroscopy of protein monolayers with plasmonic nanoantenna arrays. PNAS, 106(46):19227-19232.

[2]Arguello H, Arce GR, 2011. Code aperture optimization for spectrally agile compressive imaging. J Opt Soc Am A, 28(11):2400-2413.

[3]Arguello H, Arce GR, 2014. Colored coded aperture design by concentration of measure in compressive spectral imaging. IEEE Trans Image Process, 23(4):1896-1908.

[4]Arguello H, Correa CV, Arce GR, 2013. Fast lapped block reconstructions in compressive spectral imaging. Appl Opt, 52(10):D32-D45.

[5]Bangalore AS, Shaffer RE, Small GW, et al., 1996. Genetic algorithm-based method for selecting wavelengths and model size for use with partial least-squares regression: application to near-infrared spectroscopy. Anal Chem, 68(23):4200-4212.

[6]Bao J, Bawendi MG, 2015. A colloidal quantum dot spectrometer. Nature, 523(7558):67-70.

[7]Baraniuk RG, 2007. Compressive sensing. IEEE Signal Process Mag, 24(4):118-121.

[8]Bulygin TV, Vishnyakov GN, 1992. Spectrotomography: a new method of obtaining spectrograms of two- dimensional objects. Analytical Methods for Optical Tomography, p.315-323.

[9]Candès EJ, Wakin MB, 2008. An introduction to compressive sampling. IEEE Signal Process Mag, 25(2):21-30.

[10]Candès EJ, Romberg J, Tao T, 2006. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inform Theory, 52(2):489-509.

[11]Chaganti K, Salakhutdinov I, Avrutsky I, et al., 2006. A simple miniature optical spectrometer with a planar waveguide grating coupler in combination with a plano-convex lens. Opt Expr, 14(9):4064-4072.

[12]Chang CC, Lee HN, 2008. On the estimation of target spectrum for filter-array based spectrometers. Opt Expr, 16(2):1056-1061.

[13]Chang CC, Chen CC, Kurokawa U, et al., 2011a. Accurate sensing of LED spectra via low-cost spectrum sensors. IEEE Sens J, 11(11):2869-2877.

[14]Chang CC, Lin NT, Kurokawa U, et al., 2011b. Spectrum reconstruction for filter-array spectrum sensor from sparse template selection. Opt Eng, 50(11):114402.

[15]Chang CC, Chuang YC, Wu CT, et al., 2014. A low-cost mobile device for skin tone measurement using filter array spectrum sensor. Sensors, p.499-502.

[16]Correia J, de Graaf G, Kong SH, et al., 2000. Single-chip CMOS optical microspectrometer. Sens Actuat A Phys, 82(1-3):191-197.

[17]Craig B, Shrestha VR, Meng JJ, et al., 2018. Experimental demonstration of infrared spectral reconstruction using plasmonic metasurfaces. Opt Lett, 43(18):4481-4484.

[18]Crozier KB, Sundaramurthy A, Kino GS, et al., 2003. Optical antennas: resonators for local field enhancement. J Appl Phys, 94(7):4632-4642.

[19]Cull EC, Gehm ME, Brady DJ, et al., 2007. Dispersion multiplexing with broadband filtering for miniature spectrometers. Appl Opt, 46(3):365-374.

[20]Das AJ, Wahi A, Kothari I, et al., 2016. Ultra-portable, wireless smartphone spectrometer for rapid, non-destructive testing of fruit ripeness. Sci Rep, 6:32504.

[21]Decker JA, 1971. Experimental realization of the multiplex advantage with a Hadamard-transform spectrometer. Appl Opt, 10(3):510-514.

[22]Diaz N, Rueda H, Arguello H, 2018. Adaptive filter design via a gradient thresholding algorithm for compressive spectral imaging. Appl Opt, 57(17):4890-4900.

[23]Donoho DL, 2006. Compressed sensing. IEEE Trans Inform Theory, 52(4):1289-1306.

[24]Duarte MF, Davenport MA, Takhar D, et al., 2008. Single- pixel imaging via compressive sampling. IEEE Signal Process Mag, 25(2):83-91.

[25]Faraji-Dana M, Arbabi E, Arbabi A, et al., 2018. Compact folded metasurface spectrometer. Nat Commun, 9(1): 4196.

[26]Feller SD, Chen H, Brady DJ, et al., 2007. Multiple order coded aperture spectrometer. Opt Expr, 15(9):5625- 5630.

[27]Ford BK, Descour MR, Lynch RM, 2001. Large-image-format computed tomography imaging spectrometer for fluorescence microscopy. Opt Expr, 9(9):444-453.

[28]Galvis L, Lau D, Ma X, et al., 2017. Coded aperture design in compressive spectral imaging based on side information. Appl Opt, 56(22):6332-6340.

[29]Gao L, Wang LV, 2016. A review of snapshot multidimensional optical imaging: measuring photon tags in parallel. Phys Rep, 616:1-37.

[30]Gehm ME, McCain ST, Pitsianis NP, et al., 2006. Static two- dimensional aperture coding for multimodal, multiplex spectroscopy. Appl Opt, 45(13):2965-2974.

[31]Gehm ME, John R, Brady DJ, et al., 2007. Single-shot compressive spectral imaging with a dual-disperser architecture. Opt Expr, 15(21):14013-14027.

[32]Girard A, 1963. Spectromètre à grilles. Appl Opt, 2(1):79-87 (in French).

[33]Goel M, Whitmire E, Mariakakis A, et al., 2015. HyperCam: hyperspectral imaging for ubiquitous computing applications. Proc ACM Int Joint Conf on Pervasive and Ubiquitous Computing, p.145-156.

[34]Golay MJE, 1949. Multi-slit spectrometry. J Opt Soc Am, 39(6):437-444.

[35]Golay MJE, 1951. Static multislit spectrometry and its application to the panoramic display of infrared spectra. J Opt Soc Am, 41(7):468-472.

[36]Hagen NA, Kudenov MW, 2013. Review of snapshot spectral imaging technologies. Opt Eng, 52(9):090901.

[37]Hansen P, Strong J, 1972. High resolution Hadamard transform spectrometer. Appl Opt, 11(3):502-506.

[38]Hayes MH, 1996. Statistical Digital Signal Processing and Modeling. John Wiley & Sons, New York, USA.

[39]Hinojosa CA, Correa CV, Arguello H, et al., 2016. Compressive spectral imaging using multiple snapshot colored- mosaic detector measurements. Computational Imaging, Article 987004.

[40]Huang E, Ma Q, Liu ZW, 2017. Etalon array reconstructive spectrometry. Sci Rep, 7:40693.

[41]Jacquinot P, 1960. New developments in interference spectroscopy. Rep Prog Phys, 23(1):267-312.

[42]Kats MA, Blanchard R, Genevet P, et al., 2013. Thermal tuning of mid-infrared plasmonic antenna arrays using a phase change material. Opt Let, 38(3):368-370.

[43]Kirchhoff GR, Bunsen RW, 1861. Chemische analyse durch spectralbeobachtungen. Ann Phys Chem, 189:3370381 (in German).

[44]Kita DM, Miranda B, Favela D, et al., 2018. High-performance and scalable on-chip digital Fourier transform spectroscopy. Nat Commun, 9(1):4405.

[45]Kudenov MW, Dereniak EL, 2012. Compact real-time birefringent imaging spectrometer. Opt Expr, 20(16):17973-17986.

[46]Kuiteing SK, Coluccia G, Barducci A, et al., 2014. Compressive hyperspectral imaging using progressive total variation. IEEE Int Conf on Acoustics, Speech and Signal Processing, p.7794-7798.

[47]Kurokawa U, Choi BI, Chang CC, 2011. Filter-based miniature spectrometers: spectrum reconstruction using adaptive regularization. IEEE Sens J, 11(7):1556-1563.

[48]Li ZY, Palacios E, Butun S, et al., 2015. Visible-frequency metasurfaces for broadband anomalous reflection and high-efficiency spectrum splitting. Nano Lett, 15(3):1615-1621.

[49]Momeni B, Hosseini ES, Askari M, et al., 2009. Integrated photonic crystal spectrometers for sensing applications. Opt Commun, 282(15):3168-3171.

[50]Newton I, 1979. Opticks (2nd Ed.). Dover Publications Inc., New York, USA.

[51]Okamoto T, Yamaguchi I, 1991. Simultaneous acquisition of spectral image information. Opt Lett, 16(16):1277-1279.

[52]Oliver J, Lee W, Park S, et al., 2012. Improving resolution of miniature spectrometers by exploiting sparse nature of signals. Opt Expr, 20(3):2613-2625.

[53]Oliver J, Lee WB, Lee HN, 2013. Filters with random transmittance for improving resolution in filter-array-based spectrometers. Opt Expr, 21(4):3969-3989.

[54]Pervez NK, Cheng W, Jia Z, et al., 2010. Photonic crystal spectrometer. Opt Expr, 18(8):8277-8285.

[55]Phillips PG, Briotta DA, 1974. Hadamard-transform spectrometry of the atmospheres of Earth and Jupiter. Appl Opt, 13(10):2233-2235.

[56]Rajwade A, Kittle D, Tsai TH, et al., 2013. Coded hyperspectral imaging and blind compressive sensing. SIAM J Imag Sci, 6(2):782-812.

[57]Redding B, Liew SF, Sarma R, et al., 2013. Compact spectrometer based on a disordered photonic chip. Nat Photon, 7(9):746-751.

[58]Ren WY, Fu C, Arce GR, 2018. The first result of compressed channeled imaging spectropolarimeter. Imaging and Applied Optics, Article JTu4A.21.

[59]Rueda H, Arguello H, Arce GR, 2015. DMD-based implementation of patterned optical filter arrays for compressive spectral imaging. J Opt Soc Am A, 32(1):80-89.

[60]Shaltout A, Liu JJ, Kildishev A, et al., 2015. Photonic spin Hall effect in gap—plasmon metasurfaces for on-chip chiroptical spectroscopy. Optica, 2(10):860-863.

[61]Soldevila F, Irles E, Durán V, et al., 2013. Single-pixel polarimetric imaging spectrometer by compressive sensing. Appl Phys B, 113(4):551-558.

[62]Sun T, Kelly K, 2009. Compressive sensing hyperspectral imager. Computational Optical Sensing and Imaging, Article CTuA5.

[63]Swift RD, Wattson RB, Decker JA, et al., 1976. Hadamard transform imager and imaging spectrometer. Appl Opt, 15(6):1595-1609.

[64]Takhar D, Laska JN, Wakin MB, et al., 2006. A new compressive imaging camera architecture using optical- domain compression. Computational Imaging IV, Article 606509.

[65]Vigneau E, Devaux MF, Qannari EM, et al., 1997. Principal component regression, ridge regression and ridge principal component regression in spectroscopy calibration. J Chemomet, 11(3):239-249.

[66]Wagadarikar A, John R, Willett R, et al., 2008. Single disperser design for coded aperture snapshot spectral imaging. Appl Opt, 47(10):B44-B51.

[67]Wagadarikar AA, Pitsianis NP, Sun XB, et al., 2009. Video rate spectral imaging using a coded aperture snapshot spectral imager. Opt Expr, 17(8):6368-6388.

[68]Wang LZ, Xiong ZW, Gao DH, et al., 2015. Dual-camera design for coded aperture snapshot spectral imaging. Appl Opt, 54(4):848-858.

[69]Wang LZ, Xiong ZW, Shi GM, et al., 2017. Adaptive nonlocal sparse representation for dual-camera compressive hyperspectral imaging. IEEE Trans Patt Anal Mach Intell, 39(10):2104-2111.

[70]Wang Z, Yi S, Chen A, et al., 2019. Single-shot on-chip spectral sensors based on photonic crystal slabs. Nat Commun, 10(1):1020.

[71]Willett RM, Gehm ME, Brady DJ, 2007. Multiscale reconstruction for computational spectral imaging. Computational Imaging V, Article 64980L.

[72]Wolffenbuttel RF, 2004. State-of-the-art in integrated optical microspectrometers. IEEE Trans Instrum Meas, 53(1):197-202.

[73]Yetzbacher MK, Miller CW, Boudreau AJ, et al., 2014. Multiple-order staircase etalon spectroscopy. Next-Generation Spectroscopic Technologies VII, Article 910104.

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