Full Text:   <761>

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CLC number: TN919.8

On-line Access: 2017-10-25

Received: 2017-02-08

Revision Accepted: 2017-07-12

Crosschecked: 2017-09-15

Cited: 1

Clicked: 2761

Citations:  Bibtex RefMan EndNote GB/T7714


Lin-sen Chen


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.9 P.1250-1260


High-resolution spectral video acquisition

Author(s):  Lin-sen Chen, Tao Yue, Xun Cao, Zhan Ma, David J. Brady

Affiliation(s):  School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China; more

Corresponding email(s):   njucls@163.com, yuetao@nju.edu.cn

Key Words:  Multispectral/hyperspectral video acquisition, Snapshot, Under-sampling and reconstruction

Lin-sen Chen, Tao Yue, Xun Cao, Zhan Ma, David J. Brady. High-resolution spectral video acquisition[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(9): 1250-1260.

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%T High-resolution spectral video acquisition
%A Lin-sen Chen
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%A Xun Cao
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700098

T1 - High-resolution spectral video acquisition
A1 - Lin-sen Chen
A1 - Tao Yue
A1 - Xun Cao
A1 - Zhan Ma
A1 - David J. Brady
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
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SP - 1250
EP - 1260
%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700098

Compared with conventional cameras, spectral imagers provide many more features in the spectral domain. They have been used in various fields such as material identification, remote sensing, precision agriculture, and surveillance. Traditional imaging spectrometers use generally scanning systems. They cannot meet the demands of dynamic scenarios. This limits the practical applications for spectral imaging. Recently, with the rapid development in computational photography theory and semiconductor techniques, spectral video acquisition has become feasible. This paper aims to offer a review of the state-of-the-art spectral imaging technologies, especially those capable of capturing spectral videos. Finally, we evaluate the performances of the existing spectral acquisition systems and discuss the trends for future work.




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


[1]Abed, F.M., Amirshahi, S.H., Abed, M.R.M., 2009. Reconstruction of reflectance data using an interpolation technique. J. Opt. Soc. Am. A, 26(3):613-624.

[2]Adelson, E.H., Bergen, J.R., 1991. The plenoptic function and the elements of early vision. In: Landy, M.S., Movshon, J.A. (Eds.), Computational Models of Visual Processing. MIT Press, Cambridge, p.3-20.

[3]Arce, G.R., Brady, D.J., Carin, L., et al., 2014. Compressive coded aperture spectral imaging: an introduction. IEEE Signal Process. Mag., 31(1):105-115.

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

[5]Bioucas-Dias, J.M., Figueiredo, M.A., 2007. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans. Imag. Process., 16(12):2992-3004.

[6]Bodkin, A., Sheinis, A., Norton, A., et al., 2009. Snapshot hyperspectral imaging: the hyperpixel array camera. SPIE, 7334:73340H.

[7]Boyd, S., Parikh, N., Chu, E., et al., 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn., 3(1):1-122.

[8]Candès, E.J., Wakin, M.B., 2008. An introduction to compressive sampling. IEEE Signal Process. Mag., 25(2): 21-30.

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

[10]Cao, X., Du, H., Tong, X., et al., 2011a. A prism-mask system for multispectral video acquisition. IEEE Trans. Patt. Anal. Mach. Intell., 33(12):2423-2435.

[11]Cao, X., Tong, X., Dai, Q., et al., 2011b. High resolution multispectral video capture with a hybrid camera system. IEEE Conf. on Computer Vision and Pattern Recognition, p.297-304.

[12]Cao, X., Yue, T., Lin, X., et al., 2016. Computational snapshot multispectral cameras. IEEE Signal Process. Mag., 33(5):95-108.

[13]Chakrabarti, A., Zickler, T., 2011. Statistics of real-world hyperspectral images. IEEE Conf. on Computer Vision and Pattern Recognition, p.193-200.

[14]Descour, M., Dereniak, E., 1995. Computed-tomography imaging spectrometer: experimental calibration and reconstruction results. Appl. Opt., 34(22):4817-4826.

[15]Descour, M., Volin, C.E., Ford, B.K., et al., 2001. Snapshot hyperspectral imaging. In: Integrated Computational Imaging Systems. OSA Publishing, Washington, D.C., paper IWB4.

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

[17]Du, H., Tong, X., Cao, X., et al., 2009. A prism-based system for multispectral video acquisition. IEEE 12th Int. Conf. on Computer Vision, p.175-182.

[18]Gao, L., Kester, R.T., Hagen, N., et al., 2010. Snapshot image mapping spectrometer (IMS) with high sampling density for hyperspectral microscopy. Opt. Expr., 18(14):14330-14344.

[19]Gat, N., 2000. Imaging spectroscopy using tunable filters: a review. SPIE, 4056:50-64.

[20]Golbabaee, M., Vandergheynst, P., 2012. Compressed sensing of simultaneous low-rank and joint-sparse matrices. arXiv:1211.5058. http://arxiv.org/abs/1211.5058

[21]Green, R.O., Eastwood, M.L., Sarture, C.M., et al., 1998. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens. Environ., 65(3):227-248.

[22]Harvey, A.R., Beale, J.E., Greenaway, A.H., et al., 2000. Technology options for imaging spectrometry. Int. Symp. on Optical Science and Technology, p.13-24.

[23]Herrala, E., Okkonen, J.T., Hyvarinen, T.S., et al., 1994. Imaging spectrometer for process industry applications. SPIE, 2248:33-40.

[24]Hunicz, J., Piernikarski, D., 2001. Investigation of combustion in a gasoline engine using spectrophotometric methods. SPIE, 4516:307-314.

[25]Kindzelskii, A.L., Yang, Z.Y., Nabel, G.J., et al., 2000. Ebola virus secretory glycoprotein (sGP) diminishes FcγRIIIB-to-CR3 proximity on neutrophils. J. Immun., 164(2):953-958.

[26]Kittle, D., Choi, K., Wagadarikar, A., et al., 2010. Multiframe image estimation for coded aperture snapshot spectral imagers. Appl. Opt., 49(36):6824-6833.

[27]Lawlor, J., Fletcher-Holmes, D., Harvey, A., et al., 2002. In vivo hyperspectral imaging of human retina and optic disc. Invest. Ophthalmol. Vis. Sci., 43(13):4350-4350.

[28]Liao, X., Li, H., Carin, L., 2014. Generalized alternating projection for weighted-l2, 1 minimization with applications to model-based compressive sensing. SIAM J. Imag. Sci., 7(2):797-823.

[29]Lin, X., Liu, Y., Wu, J., et al., 2014a. Spatial-spectral encoded compressive hyperspectral imaging. ACM Trans. Graph., 33(6), Article 233.

[30]Lin, X., Wetzstein, G., Liu, Y., et al., 2014b. Dual-coded compressive hyperspectral imaging. Opt. Lett., 39(7):2044-2047.

[31]Ma, C., Cao, X., Wu, R., et al., 2014. Content-adaptive high-resolution hyperspectral video acquisition with a hybrid camera system. Opt. Lett., 39(4):937-940.

[32]Mansfield, C.L., 2005. Seeing into the Past. http://www. linebreak nasa.gov/vision/earth/technologies/scrolls.html

[33]Mitchell, P.A., 1995. Hyperspectral digital imagery collection experiment (HYDICE). SPIE, 2587:70-95.

[34]Mooney, J.M., Vickers, V.E., An, M., et al., 1997. High-throughput hyperspectral infrared camera. J. Opt. Soc. Am. A, 14(11):2951-2961.

[35]Morovic, P., Finlayson, G.D., 2006. Metamer-set-based approach to estimating surface reflectance from camera RGB. J. Opt. Soc. Am. A, 23(8):1814-1822.

[36]Morris, H.R., Hoyt, C.C., Treado, P.J., 1994. Imaging spectrometers for fluorescence and Raman microscopy: acousto-optic and liquid crystal tunable filters. Appl. Spectr., 48(7):857-866.

[37]Nguyen, R.M., Prasad, D.K., Brown, M.S., 2014. Training-based spectral reconstruction from a single RGB image. European Conf. on Computer Vision, p.186-201.

[38]Oh, W.S., Brown, M.S., Pollefeys, M., et al., 2016. Do it yourself hyperspectral imaging with everyday digital cameras. IEEE Conf. on Computer Vision and Pattern Recognition, p.2461-2469.

[39]Radon, J., 1917. Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten. Akad. Wiss., 69:262-277 (in German).

[40]Røslett, B., 2004. All you ever wanted to know about digital UV and IR photography, but could not afford to ask. http://www.naturfotograf.com/UV_IR_rev00.html

[41]Schechner, Y.Y., Nayar, S.K., 2002. Generalized mosaicing: wide field of view multispectral imaging. IEEE Trans. Patt. Anal. Mach. Intell., 24(10):1334-1348.

[42]Shepp, L.A., Vardi, Y., 1982. Maximum likelihood reconstruction for emission tomography. IEEE Trans. Med. Imag., 1(2):113-122.

[43]Su, L., Zhou, Z., Yuan, Y., et al., 2015. A snapshot light field imaging spectrometer. Opt.-Int. J. Light Electr. Opt., 126(9):877-881.

[44]Wagadarikar, A.A., Pitsianis, N.P., Sun, X., et al., 2009. Video rate spectral imaging using a coded aperture snapshot spectral imager. Opt. Expr., 17(8):6368-6388.

[45]Willett, R.M., Duarte, M.F., Davenport, M.A., et al., 2014. Sparsity and structure in hyperspectral imaging: sensing, reconstruction, and target detection. IEEE Signal Process. Mag., 31(1):116-126.

[46]Wu, Y., Mirza, I.O., Arce, G.R., et al., 2011. Development of a digital-micromirror-device-based multishot snapshot spectral imaging system. Opt. Lett., 36(14):2692-2694.

[47]Yamaguchi, M., Haneishi, H., Fukuda, H., et al., 2006. High-fidelity video and still-image communication based on spectral information: natural vision system and its applications. SPIE, 6062:60620G.

[48]Yasuma, F., Mitsunaga, T., Iso, D., et al., 2010. Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum. IEEE Trans. Imag. Process., 19(9):2241-2253.

[49]Zhou, Z., Yuan, Y., Bin, X.L., 2010. Light field imaging spectrometer: conceptual design and simulated performance. Frontiers in Optics/Laser Science XXVI, paper FThM3.

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