Full Text:   <1885>

Summary:  <1594>

CLC number: TN919.8

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2017-09-15

Cited: 1

Clicked: 6070

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Lin-sen Chen

http://orcid.org/0000-0002-1259-135X

-   Go to

Article info.
Open peer comments

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

http://doi.org/10.1631/FITEE.1700098


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.

@article{title="High-resolution spectral video acquisition",
author="Lin-sen Chen, Tao Yue, Xun Cao, Zhan Ma, David J. Brady",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="9",
pages="1250-1260",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700098"
}

%0 Journal Article
%T High-resolution spectral video acquisition
%A Lin-sen Chen
%A Tao Yue
%A Xun Cao
%A Zhan Ma
%A David J. Brady
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 9
%P 1250-1260
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700098

TY - JOUR
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
IS - 9
SP - 1250
EP - 1260
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700098


Abstract: 
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.

高分辨率光谱视频采集

概要:与传统视频采集技术相比,高分辨率光谱视频采集技术能够为精细农业、环境监测、遥感、军事刑侦等诸多重要领域提供高维度、高精度场景特征信息。传统光谱仪主要采用的扫描系统无法满足动态场景下的采集需求,从而限制了光谱成像技术的应用范围。近来,得益于计算摄像理论和半导体技术的快速发展,光谱视频采集技术成为可能。本文首先详细介绍了传统光谱仪的基本理论与工作原理。其次,针对光谱视频采集的核心问题,从成像理论、系统结构、关键器件等方面回顾了近年出现的瞬拍式光谱采集技术。从进一步提升光谱视频采集精度的角度出发,通过主观质量评价与客观指标比较,分析和总结了各光谱成像系统的性能优势与弊端,同时讨论了计算摄像理论、半导体制备工艺对光谱视频采集技术发展的关键作用。

关键词:多光谱/高光谱视频采集;单次曝光;采样与重建

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

Reference

[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.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE