Snapshot hyperspectral imaging via spectral basis multiplexing in Fourier domain

Chao Deng; Xuemei Hu; Jinli Suo; Yuanlong Zhang; Zhili Zhang; Qionghai Dai

doi:10.1364/OE.26.032509

Snapshot hyperspectral imaging via spectral basis multiplexing in Fourier domain

Chao Deng, Xuemei Hu, Jinli Suo, Yuanlong Zhang, Zhili Zhang, and Qionghai Dai

Optics Express
Vol. 26,
Issue 25,
pp. 32509-32521
(2018)
•https://doi.org/10.1364/OE.26.032509

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Chao Deng, Xuemei Hu, Jinli Suo, Yuanlong Zhang, Zhili Zhang, and Qionghai Dai, "Snapshot hyperspectral imaging via spectral basis multiplexing in Fourier domain," Opt. Express 26, 32509-32521 (2018)

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Related Topics
Table of Contents Category
- Imaging Systems, Microscopy, and Displays
Optics & Photonics Topics
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The topics in this list come from the OSA Optics and Photonics Topics applied to this article.

About this Article
History
- Original Manuscript: July 31, 2018
- Revised Manuscript: October 14, 2018
- Manuscript Accepted: October 17, 2018
- Published: November 28, 2018

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Open Access

Abstract

Hyperspectral imaging is an important tool having been applied in various fields, but still limited in observation of dynamic scenes. In this paper, we propose a snapshot hyperspectral imaging technique which exploits both spectral and spatial sparsity of natural scenes. Under the computational imaging scheme, we conduct spectral dimension reduction and spatial frequency truncation to the hyperspectral data cube and snapshot it in a low cost manner. Specifically, we modulate the spectral variations by several broadband spectral filters, and then map these modulated images into different regions in the Fourier domain. The encoded image compressed in both spectral and spatial are finally collected by a monochrome detector. Correspondingly, the reconstruction is essentially a Fourier domain extraction and spectral dimensional back projection with low computational load. This Fourier-spectral multiplexing in a 2D sensor simplifies both the encoding and decoding process, and makes hyperspectral data captured in a low cost manner. We demonstrate the high performance of our method by quantitative evaluation on simulation data and build a prototype system experimentally for further validation.

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References

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Publication

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K. Dorozynska and E. Kristensson, “Implementation of a multiplexed structured illumination method to achieve snapshot multispectral imaging,” Opt. Express 25, 17211–17226 (2017).
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2018 (2)

Y. Zhang, J. Suo, Y. Wang, and Q. Dai, “Doubling the pixel count limitation of single-pixel imaging via sinusoidal amplitude modulation,” Opt. Express 26, 6929–6942 (2018).
[Crossref] [PubMed]

Z. Zhang, S. Liu, J. Peng, M. Yao, G. Zheng, and J. Zhong, “Simultaneous spatial, spectral, and 3d compressive imaging via efficient fourier single-pixel measurements,” Optica 5, 315–319 (2018).
[Crossref]

2017 (3)

K. Dorozynska and E. Kristensson, “Implementation of a multiplexed structured illumination method to achieve snapshot multispectral imaging,” Opt. Express 25, 17211–17226 (2017).
[Crossref] [PubMed]

E. Kristensson, Z. Li, E. Berrocal, M. Richter, and M. Aldén, “Instantaneous 3D imaging of flame species using coded laser illumination,” Proc. Combust. Inst. 36, 4585–4591 (2017).
[Crossref]

A. Ehn, J. Bood, Z. Li, E. Berrocal, M. Aldén, and E. Kristensson, “FRAME: femtosecond videography for atomic and molecular dynamics,” Light. Sci. Appl. 6, 17045 (2017).
[Crossref]

2016 (1)

L. Bian, J. Suo, X. Hu, F. Chen, and Q. Dai, “Efficient single pixel imaging in Fourier space,” J. Opt. 18, 085704 (2016).
[Crossref]

2014 (3)

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

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31, 105–115 (2014).
[Crossref]

P. Wang and R. Menon, “Computational spectrometer based on a broadband diffractive optic,” Opt. Express 22, 14575–14587 (2014).
[Crossref] [PubMed]

2013 (2)

P. Llull, X. Liao, X. Yuan, J. Yang, D. Kittle, L. Carin, G. Sapiro, and D. J. Brady, “Coded aperture compressive temporal imaging,” Opt. Express 21, 10526–10545 (2013).
[Crossref] [PubMed]

N. Hagen and M. W. Kudenov, “Review of snapshot spectral imaging technologies,” Opt. Eng. 52, 090901 (2013).
[Crossref]

2011 (2)

A. J. Reiter and S. C. Kong, “Combustion and emissions characteristics of compression-ignition engine using dual ammonia-diesel fuel,” Fuel 90, 87–97 (2011).
[Crossref]

R. T. Kester, N. Bedard, L. Gao, and T. S. Tkaczyk, “Real-time snapshot hyperspectral imaging endoscope,” J. Biomed. Opt. 16, 056005 (2011).
[Crossref] [PubMed]

2010 (4)

A. Gorman, D. W. Fletcher-Holmes, and A. R. Harvey, “Generalization of the lyot filter and its application to snapshot spectral imaging,” Opt. Express 18, 5602–5608 (2010).
[Crossref] [PubMed]

M. W. Kudenov, M. E. Jungwirth, E. L. Dereniak, and G. R. Gerhart, “White-light Sagnac interferometer for snapshot multispectral imaging,” Appl. Opt. 49, 4067–4076 (2010).
[Crossref] [PubMed]

M. W. Kudenov and E. L. Dereniak, “Compact snapshot birefringent imaging Fourier transform spectrometer,” Proc. SPIE 7812, 6–12 (2010).

F. Yasuma, T. Mitsunaga, D. Iso, and S. K. Nayar, “Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum,” IEEE Trans. Image Process. 19, 2241–2253 (2010).
[Crossref] [PubMed]

2009 (4)

R. Dosselmann and X. Yang, “A comprehensive assessment of the structural similarity index,” Signal, Image Video Process. 5, 81–91 (2009).
[Crossref]

Z. Wang and Q. Li, “Information Content Weighting for Perceptual Image Quality Assessment,” IEEE Trans. Image Process. 201185–1198 (2009)
[Crossref]

L. Gao, R. T. Kester, and T. S. Tkaczyk, “Compact image slicing spectrometer (iss) for hyperspectral fluorescence microscopy,” Opt. Express 17, 12293–12308 (2009).
[Crossref] [PubMed]

S. Delalieux, A. Auwerkerken, W. W. Verstraeten, B. Somers, R. Valcke, S. Lhermitte, J. Keulemans, and P. Coppin, “Hyperspectral reflectance and fluorescence imaging to detect scab induced stress in apple leaves,” Remote Sens. 1, 858–874 (2009).
[Crossref]

2008 (1)

2007 (2)

M. Gehm, R. John, D. Brady, R. Willett, and T. Schulz, “Single-shot compressive spectral imaging with a dual-disperser architecture,” Opt. Express 15, 14013–14027 (2007).
[Crossref] [PubMed]

J. D. Matchett, R. I. Billmers, E. J. Billmers, and M. E. Ludwig, “Volume holographic beam splitter for hyperspectral imaging applications,” Proc. SPIE 6668, 66680K (2007).
[Crossref]

2006 (1)

G. Zavattini, S. Vecchi, G. Mitchell, U. Weisser, R. M. Leahy, B. J. Pichler, D. J. Smith, and S. R. Cherry, “A hyperspectral fluorescence system for 3D in vivo optical imaging,” Phys. Med. Biol. 51, 2029 (2006).
[Crossref] [PubMed]

2005 (2)

J. P. Bibring, Y. Langevin, A. Gendrin, B. Gondet, F. Poulet, M. Berthé, A. Soufflot, R. Arvidson, N. Mangold, J. Mustard, P. Drossart, and the OMEGA team, “Mars surface diversity as revealed by the omega/mars express observations,” Science 307, 1576–1581 (2005).
[Crossref] [PubMed]

R. G. Sellar and G. D. Boreman, “Comparison of relative signal-to-noise ratios of different classes of imaging spectrometer,” Appl. Opt. 44, 1614–1624 (2005).
[Crossref] [PubMed]

2004 (3)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[Crossref] [PubMed]

T. Vo-Dinh, “A hyperspectral imaging system for in vivo optical diagnostics,” IEEE Eng. Med. Biol. Mag. 23, 40–49 (2004).
[Crossref] [PubMed]

M. J. Barnsley, J. J. Settle, M. A. Cutter, D. R. Lobb, and F. Teston, “The proba/chris mission: A low-cost smallsat for hyperspectral multiangle observations of the earth surface and atmosphere,” IEEE Trans. Geosci. Remote Sens. 42, 1512–1520 (2004).
[Crossref]

2003 (2)

Z. Pan, G. Healey, M. Prasad, and B. Tromberg, “Face recognition in hyperspectral images,” IEEE Trans. on Pattern Anal. Mach. Intell. 25, 1552–1560 (2003).
[Crossref]

A. R. Harvey and D. W. Fletcher-Holmes, “High-throughput snapshot spectral imaging in two dimensions,” Proc. SPIE 4959, 4959136 (2003).

2002 (1)

M. Rabbani, “JPEG2000: Image compression fundamentals, standards and practice,” J. Electron. Imaging 11, 286 (2002).
[Crossref]

2000 (1)

V. Backman, M. B. Wallace, L. Perelman, J. Arendt, R. Gurjar, M. Müller, Q. Zhang, G. Zonios, E. Kline, T. McGillican, S. Shapshay, T. Valdez, K. Badizadegan, J. M. Crawford, M. Fitzmaurice, S. Kabani, H. S. Levin, M. Seiler, R. R. Dasari, I. Itzkan, J. Van Dam, and M. S. Feld, “Detection of preinvasive cancer cells,” Nature 406, 35–36 (2000).
[Crossref] [PubMed]

1998 (1)

C. E. Volin, B. K. Ford, M. R. Descour, J. P. Garcia, D. W. Wilson, P. D. Maker, and G. H. Bearman, “High-speed spectral imager for imaging transient fluorescence phenomena,” Appl. Opt. 37, 8112–8119 (1998).
[Crossref]

1996 (2)

L. Weitzel, A. Krabbe, H. Kroker, N. Thatte, L. Tacconi-Garman, M. Cameron, and R. Genzel, “3d: The next generation near-infrared imaging spectrometer,” Astron. Astrophys. Suppl. Ser. 119, 531–546 (1996).
[Crossref]

M. T. Eismann, C. R. Schwartz, J. N. Cederquist, J. A. Hackwell, and R. J. Huppi, “Comparison of infrared imaging hyperspectral sensors for military target detection applications,” Proc. SPIE 2819, 91–102(1996).
[Crossref]

1994 (1)

A. Hirai, T. Inoue, K. Itoh, and Y. Ichioka, “Application of multiple-image Fourier transform spectral imaging to measurement of fast phenomena,” Opt. Rev. 1, 205–207 (1994).
[Crossref]

1991 (2)

T. Okamoto and I. Yamaguchi, “Simultaneous acquisition of spectral image information,” Opt. Lett. 16, 1277–1279 (1991).
[Crossref] [PubMed]

P. Kauranen, S. Andersson-Engels, and S. Svanberg, “Spatial mapping of flame radical emission using a spectroscopic multi-colour imaging system,” Appl. Phys. B 53, 260–264 (1991).
[Crossref]

1989 (1)

J. P. Parkkinen, J. Hallikainen, and T. Jaaskelainen, “Characteristic spectra of Munsell colors,” J. Opt. Soc. Am. A 6, 318–322 (1989).
[Crossref]

1985 (1)

A. F. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for earth remote sensing,” Science 228, 1147–1153 (1985).
[Crossref] [PubMed]

Aldén, M.

E. Kristensson, Z. Li, E. Berrocal, M. Richter, and M. Aldén, “Instantaneous 3D imaging of flame species using coded laser illumination,” Proc. Combust. Inst. 36, 4585–4591 (2017).
[Crossref]

A. Ehn, J. Bood, Z. Li, E. Berrocal, M. Aldén, and E. Kristensson, “FRAME: femtosecond videography for atomic and molecular dynamics,” Light. Sci. Appl. 6, 17045 (2017).
[Crossref]

Andersson-Engels, S.

P. Kauranen, S. Andersson-Engels, and S. Svanberg, “Spatial mapping of flame radical emission using a spectroscopic multi-colour imaging system,” Appl. Phys. B 53, 260–264 (1991).
[Crossref]

Arce, G. R.

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31, 105–115 (2014).
[Crossref]

Ardouin, J. P.

J. P. Ardouin, J. Lévesque, and T. A. Rea, “A demonstration of hyperspectral image exploitation for military applications,” in Proceedings of IEEE Conference on Information Fusion (IEEE, 2007), pp. 1–8.

Arendt, J.

Arguello, H.

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31, 105–115 (2014).
[Crossref]

Arvidson, R.

Auwerkerken, A.

Backman, V.

Badizadegan, K.

Barnsley, M. J.

Bearman, G. H.

Bedard, N.

R. T. Kester, N. Bedard, L. Gao, and T. S. Tkaczyk, “Real-time snapshot hyperspectral imaging endoscope,” J. Biomed. Opt. 16, 056005 (2011).
[Crossref] [PubMed]

Berrocal, E.

E. Kristensson, Z. Li, E. Berrocal, M. Richter, and M. Aldén, “Instantaneous 3D imaging of flame species using coded laser illumination,” Proc. Combust. Inst. 36, 4585–4591 (2017).
[Crossref]

A. Ehn, J. Bood, Z. Li, E. Berrocal, M. Aldén, and E. Kristensson, “FRAME: femtosecond videography for atomic and molecular dynamics,” Light. Sci. Appl. 6, 17045 (2017).
[Crossref]

Berthé, M.

Bian, L.

L. Bian, J. Suo, X. Hu, F. Chen, and Q. Dai, “Efficient single pixel imaging in Fourier space,” J. Opt. 18, 085704 (2016).
[Crossref]

Bibring, J. P.

Billmers, E. J.

J. D. Matchett, R. I. Billmers, E. J. Billmers, and M. E. Ludwig, “Volume holographic beam splitter for hyperspectral imaging applications,” Proc. SPIE 6668, 66680K (2007).
[Crossref]

Billmers, R. I.

J. D. Matchett, R. I. Billmers, E. J. Billmers, and M. E. Ludwig, “Volume holographic beam splitter for hyperspectral imaging applications,” Proc. SPIE 6668, 66680K (2007).
[Crossref]

Bood, J.

A. Ehn, J. Bood, Z. Li, E. Berrocal, M. Aldén, and E. Kristensson, “FRAME: femtosecond videography for atomic and molecular dynamics,” Light. Sci. Appl. 6, 17045 (2017).
[Crossref]

Boreman, G. D.

R. G. Sellar and G. D. Boreman, “Comparison of relative signal-to-noise ratios of different classes of imaging spectrometer,” Appl. Opt. 44, 1614–1624 (2005).
[Crossref] [PubMed]

Bovik, A. C.

Brady, D.

M. Gehm, R. John, D. Brady, R. Willett, and T. Schulz, “Single-shot compressive spectral imaging with a dual-disperser architecture,” Opt. Express 15, 14013–14027 (2007).
[Crossref] [PubMed]

Brady, D. J.

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31, 105–115 (2014).
[Crossref]

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Appl. Opt. (3)

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IEEE Eng. Med. Biol. Mag. (1)

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IEEE Signal Process. Mag. (1)

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IEEE Trans. Geosci. Remote Sens. (1)

IEEE Trans. Image Process. (4)

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IEEE Trans. on Pattern Anal. Mach. Intell. (1)

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J. Biomed. Opt. (1)

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J. Opt. (1)

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J. Opt. Soc. Am. A (1)

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Light. Sci. Appl. (1)

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Nature (1)

Opt. Eng. (1)

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Opt. Express (7)

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L. Gao, R. T. Kester, and T. S. Tkaczyk, “Compact image slicing spectrometer (iss) for hyperspectral fluorescence microscopy,” Opt. Express 17, 12293–12308 (2009).
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Opt. Lett. (1)

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Supplementary Material (3)

Name	Description
» Visualization 1	Santorini_Island
» Visualization 2	Rotating
» Visualization 3	diffusion

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Fig. 1 (a) The scheme of the proposed hyperspectral imaging system. The hyperspectral data is spectrally filtered and projected into six images: x₁, x₂, · · · , x₆, and then codified by six sinusoidal patterns s₁, s₂, · · · , s₆, respectively. The six sinusoidal patterns shift the Fourier distribution of six projected images away from the origin into six different regions, and the gray scale camera captures the modulated images in an add-up way. The hyperspectral images are reconstructed through a two-stage method, including: Fourier spectrum demultiplexing and linear system reconstruction. (b) The centralized spreads of the nature images’ Fourier coefficients. The reconstruction (bottom image) from 6.25% (0.25²) Fourier coefficients locating at the centroid region (upper image) is quite clear.

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Fig. 2 The PSNR, RMSE and SSIM scores of different cropping sizes, ranging from 0.05 to 0.4 of image width.

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Fig. 3 Performance promotion by GAP. Upper part: six channels reconstructed after GAP optimization. Ch.1 ∼Ch.6 represent the six projections, and the close-ups compare the de-aliasing before and after using GAP. w/o: without GAP optimization; w: with GAP optimization. Bottom part: PSNR, SSIM and RMSE promotion through GAP optimization.

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Fig. 4 The PSNR of the reconstruction and two examples from the CAVE multi-spectral database. In each example, the upper row is the ground truth in 500 nm, 600 nm, and 700 nm, respectively and the lower row is the corresponding reconstruction.

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Fig. 5 The imaging scheme of proposed method. (a) The optical diagram including transmissive and reflective mode (the beam splitter is omitted in reflective mode). L1 and L2: converging lens composing a 4f system. CW: color wheel. GSM: gray scale sinusoidal modulation. L3 and L4: Converging lens. (b) The imaging setup including both transmissive and reflective mode. DMD: digital micromirror device; FL: Fourier lens. (c) The transmissive response of CW. (d) The GSM module implementing fast gray scale sinusoidal modulation.

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Fig. 6 The experimental reconstruction of a flower film with five strips of different transparent color paper. The RGB image of the object, the measurement together with its Fourier space and the reconstructed five spectrum of transparent color paper are displayed in the left part. The solid red, dashed black and dotted blue curve are the true spectrum, reconstruction with (w) GAP and without (w/o) GAP, respectively. The reconstructed hyperspectral images are displayed in the middle. The spectral range is 400 nm ∼ 700 nm. We highlight the distinction of reconstruction without and with GAP optimization in the right part.

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Fig. 7 Hyperspectral reconstruction of regular motion: (a): a color film of Santorini Island mounted on a translation stage moving at a constant speed of 3.7 mm/s; (b): a color film of a landscape fixed on a rotary translation stage with angular speed of 0.023 rad/s.

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Fig. 8 Hyperspectral reconstruction of the diffusion process of color pigments poured into clean water. Three out of 77 frames are displayed.

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Tables (1)

Table 1 The performance comparison of different hyperspectral imaging methods

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Equations (9)

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x_{i} = \int_{λ} I_{i} (λ) r (λ) d λ,

r (λ) = \sum_{j = 1}^{J} α_{j} b_{j} (λ),

x_{i} = \sum_{j = 1}^{J} α_{j} \int_{λ} I_{i} (λ) b_{j} (λ) d λ .

s_{i} = 1 + cos (p \cdot ω_{i}),

y = \sum_{i = 1}^{J} x_{i} ⊙ s_{i} .

min_{W} {‖ W ‖}_{ℓ_{2, 1}^{𝒢 β,}} subject to Y = SX and X = TW,

{‖ W ‖}_{ℓ_{2, 1}^{𝒢 β}} = \sum_{k = 1}^{m} β_{k} {‖ W_{𝒢_{k}} ‖}_{2},

arg min_{α} {‖ \hat{x} - F α ‖}_{2}^{2} + η {‖ \frac{\partial^{2} r (λ)}{\partial λ^{2}} ‖}_{2}^{2} s . t . r (λ) \geq 0 .

min max_{\begin{array}{l} 1 \leq i, j \leq 6, \\ i \neq j \end{array}} corr (I_{i}, I_{j}),

	Spatial Res.	Spectral Res.	Computational Load	Cost
IFS	low	low	low	high
MSBS	high	low	low	high
IRIS	low	low	low	high
MSI	high	low	low	high
CTIS	low	low	low	high
MAFC	low	high	low	high
IMS	low	high	low	high
SHIFT	low	high	low	high
CASSI	high	high	high	low
FRAME	high	low	low	low
FSM	high	high	low	low