Abstract

Pushbroom hyperspectral imaging (HSI) has been used in many areas from air to land. However, its inherent operational drawback of the bulky slit leads to a limited field of view (FOV) and high energy consumption. Accordingly, a new and versatile HSI system is proposed by employing a smart digital micromirror device (DMD) to replace the mechanical scanning component. Moreover, tunable spatial and spectral resolution is implemented through adjusting the on-chip scanning linewidth and adopting the pixel fusion method, respectively. Meanwhile, three scanning modes including rough scanning, fine scanning, and regional scanning are achieved. These multiple choices increase the system’s flexibility, universality, and intelligence, which is attractive for practically different applications, especially for military and remote sensing fields in need of a large FOV, and medical and food fields in need of tunable resolution for various samples.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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    [Crossref] [PubMed]
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2018 (2)

A. Siedliska, P. Baranowski, M. Zubik, W. Mazurek, and B. Sosnowska, “Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging,” Postharvest Biol. Technol. 139, 115–126 (2018).
[Crossref]

T. Yoon, C. S. Kim, K. Kim, and J. R. Choi, “Emerging applications of digital micromirror devices in biophotonic fields,” Opt. Laser Technol. 104, 17–25 (2018).
[Crossref]

2017 (2)

J. Reimers, A. Bauer, K. P. Thompson, and J. P. Rolland, “Freeform spectrometer enabling increased compactness,” Light Sci. Appl. 6(7), e17026 (2017).
[Crossref] [PubMed]

A. Jullien, R. Pascal, U. Bortolozzo, N. Forget, and S. Residori, “High-resolution hyperspectral imaging with cascaded liquid crystal cells,” Optica 4(4), 400–405 (2017).
[Crossref]

2016 (4)

S. Schuhladen, K. Banerjee, M. Stürmer, P. Müller, U. Wallrabe, and H. Zappe, “Variable optofluidic slit aperture,” Light Sci. Appl. 5(1), e16005 (2016).
[Crossref] [PubMed]

M. Dunlop-Gray, P. K. Poon, D. Golish, E. Vera, and M. E. Gehm, “Experimental demonstration of an adaptive architecture for direct spectral imaging classification,” Opt. Express 24(16), 18307–18321 (2016).
[Crossref] [PubMed]

J. H. Cheng, D. W. Sun, and H. Pu, “Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle,” Food Chem. 197(Pt A), 855–863 (2016).
[Crossref] [PubMed]

K. Kamruzzaman, Y. Makino, and S. Oshita, “Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning,” J. Food Eng. 170(7), 8–15 (2016).
[Crossref]

2015 (1)

W. Jahr, B. Schmid, C. Schmied, F. O. Fahrbach, and J. Huisken, “Hyperspectral light sheet microscopy,” Nat. Commun. 6(1), 7990 (2015).
[Crossref] [PubMed]

2014 (3)

2013 (1)

N. Hagen and M. Kudenov, “Review of snapshot spectral imaging technologies,” Opt. Eng. 52(9), 090901 (2013).
[Crossref]

2011 (2)

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102(4), 852–857 (2011).
[Crossref] [PubMed]

Y. Wu, I. O. Mirza, G. R. Arce, and D. W. Prather, “Development of a digital-micromirror-device-based multishot snapshot spectral imaging system,” Opt. Lett. 36(14), 2692–2694 (2011).
[Crossref] [PubMed]

2010 (1)

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

2006 (1)

Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: principles and applications,” Cytometry A 69(8), 735–747 (2006).
[Crossref] [PubMed]

2005 (2)

R. G. Sellar and G. D. Boreman, “Classification of imaging spectrometers for remote sensing applications,” Opt. Eng. 44(1), 013602 (2005).
[Crossref]

R. G. Sellar and G. D. Boreman, “Comparison of relative signal-to-noise ratios of different classes of imaging spectrometer,” Appl. Opt. 44(9), 1614–1624 (2005).
[Crossref] [PubMed]

Akbari, H.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102(4), 852–857 (2011).
[Crossref] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

Arce, G. R.

Banerjee, K.

S. Schuhladen, K. Banerjee, M. Stürmer, P. Müller, U. Wallrabe, and H. Zappe, “Variable optofluidic slit aperture,” Light Sci. Appl. 5(1), e16005 (2016).
[Crossref] [PubMed]

Baranowski, P.

A. Siedliska, P. Baranowski, M. Zubik, W. Mazurek, and B. Sosnowska, “Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging,” Postharvest Biol. Technol. 139, 115–126 (2018).
[Crossref]

Bauer, A.

J. Reimers, A. Bauer, K. P. Thompson, and J. P. Rolland, “Freeform spectrometer enabling increased compactness,” Light Sci. Appl. 6(7), e17026 (2017).
[Crossref] [PubMed]

Bertolotti, J.

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(9), 1614–1624 (2005).
[Crossref] [PubMed]

R. G. Sellar and G. D. Boreman, “Classification of imaging spectrometers for remote sensing applications,” Opt. Eng. 44(1), 013602 (2005).
[Crossref]

Bortolozzo, U.

Cheng, J. H.

J. H. Cheng, D. W. Sun, and H. Pu, “Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle,” Food Chem. 197(Pt A), 855–863 (2016).
[Crossref] [PubMed]

Choi, J. R.

T. Yoon, C. S. Kim, K. Kim, and J. R. Choi, “Emerging applications of digital micromirror devices in biophotonic fields,” Opt. Laser Technol. 104, 17–25 (2018).
[Crossref]

Dai, Q.

Dunlop-Gray, M.

Fahrbach, F. O.

W. Jahr, B. Schmid, C. Schmied, F. O. Fahrbach, and J. Huisken, “Hyperspectral light sheet microscopy,” Nat. Commun. 6(1), 7990 (2015).
[Crossref] [PubMed]

Fei, B.

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19(1), 10901 (2014).
[Crossref] [PubMed]

Forget, N.

Garini, Y.

Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: principles and applications,” Cytometry A 69(8), 735–747 (2006).
[Crossref] [PubMed]

Gehm, M. E.

Golish, D.

Goorden, S. A.

Hagen, N.

N. Hagen and M. Kudenov, “Review of snapshot spectral imaging technologies,” Opt. Eng. 52(9), 090901 (2013).
[Crossref]

Huisken, J.

W. Jahr, B. Schmid, C. Schmied, F. O. Fahrbach, and J. Huisken, “Hyperspectral light sheet microscopy,” Nat. Commun. 6(1), 7990 (2015).
[Crossref] [PubMed]

Jahr, W.

W. Jahr, B. Schmid, C. Schmied, F. O. Fahrbach, and J. Huisken, “Hyperspectral light sheet microscopy,” Nat. Commun. 6(1), 7990 (2015).
[Crossref] [PubMed]

Jullien, A.

Kamruzzaman, K.

K. Kamruzzaman, Y. Makino, and S. Oshita, “Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning,” J. Food Eng. 170(7), 8–15 (2016).
[Crossref]

Kim, C. S.

T. Yoon, C. S. Kim, K. Kim, and J. R. Choi, “Emerging applications of digital micromirror devices in biophotonic fields,” Opt. Laser Technol. 104, 17–25 (2018).
[Crossref]

Kim, K.

T. Yoon, C. S. Kim, K. Kim, and J. R. Choi, “Emerging applications of digital micromirror devices in biophotonic fields,” Opt. Laser Technol. 104, 17–25 (2018).
[Crossref]

Kojima, K.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102(4), 852–857 (2011).
[Crossref] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

Kosugi, Y.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102(4), 852–857 (2011).
[Crossref] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

Kudenov, M.

N. Hagen and M. Kudenov, “Review of snapshot spectral imaging technologies,” Opt. Eng. 52(9), 090901 (2013).
[Crossref]

Lin, X.

Liu, Y.

Lu, G.

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19(1), 10901 (2014).
[Crossref] [PubMed]

Makino, Y.

K. Kamruzzaman, Y. Makino, and S. Oshita, “Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning,” J. Food Eng. 170(7), 8–15 (2016).
[Crossref]

Mazurek, W.

A. Siedliska, P. Baranowski, M. Zubik, W. Mazurek, and B. Sosnowska, “Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging,” Postharvest Biol. Technol. 139, 115–126 (2018).
[Crossref]

McNamara, G.

Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: principles and applications,” Cytometry A 69(8), 735–747 (2006).
[Crossref] [PubMed]

Mirza, I. O.

Mosk, A. P.

Müller, P.

S. Schuhladen, K. Banerjee, M. Stürmer, P. Müller, U. Wallrabe, and H. Zappe, “Variable optofluidic slit aperture,” Light Sci. Appl. 5(1), e16005 (2016).
[Crossref] [PubMed]

Oshita, S.

K. Kamruzzaman, Y. Makino, and S. Oshita, “Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning,” J. Food Eng. 170(7), 8–15 (2016).
[Crossref]

Pascal, R.

Poon, P. K.

Prather, D. W.

Pu, H.

J. H. Cheng, D. W. Sun, and H. Pu, “Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle,” Food Chem. 197(Pt A), 855–863 (2016).
[Crossref] [PubMed]

Reimers, J.

J. Reimers, A. Bauer, K. P. Thompson, and J. P. Rolland, “Freeform spectrometer enabling increased compactness,” Light Sci. Appl. 6(7), e17026 (2017).
[Crossref] [PubMed]

Residori, S.

Rolland, J. P.

J. Reimers, A. Bauer, K. P. Thompson, and J. P. Rolland, “Freeform spectrometer enabling increased compactness,” Light Sci. Appl. 6(7), e17026 (2017).
[Crossref] [PubMed]

Schmid, B.

W. Jahr, B. Schmid, C. Schmied, F. O. Fahrbach, and J. Huisken, “Hyperspectral light sheet microscopy,” Nat. Commun. 6(1), 7990 (2015).
[Crossref] [PubMed]

Schmied, C.

W. Jahr, B. Schmid, C. Schmied, F. O. Fahrbach, and J. Huisken, “Hyperspectral light sheet microscopy,” Nat. Commun. 6(1), 7990 (2015).
[Crossref] [PubMed]

Schuhladen, S.

S. Schuhladen, K. Banerjee, M. Stürmer, P. Müller, U. Wallrabe, and H. Zappe, “Variable optofluidic slit aperture,” Light Sci. Appl. 5(1), e16005 (2016).
[Crossref] [PubMed]

Sellar, R. G.

R. G. Sellar and G. D. Boreman, “Comparison of relative signal-to-noise ratios of different classes of imaging spectrometer,” Appl. Opt. 44(9), 1614–1624 (2005).
[Crossref] [PubMed]

R. G. Sellar and G. D. Boreman, “Classification of imaging spectrometers for remote sensing applications,” Opt. Eng. 44(1), 013602 (2005).
[Crossref]

Siedliska, A.

A. Siedliska, P. Baranowski, M. Zubik, W. Mazurek, and B. Sosnowska, “Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging,” Postharvest Biol. Technol. 139, 115–126 (2018).
[Crossref]

Sosnowska, B.

A. Siedliska, P. Baranowski, M. Zubik, W. Mazurek, and B. Sosnowska, “Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging,” Postharvest Biol. Technol. 139, 115–126 (2018).
[Crossref]

Stürmer, M.

S. Schuhladen, K. Banerjee, M. Stürmer, P. Müller, U. Wallrabe, and H. Zappe, “Variable optofluidic slit aperture,” Light Sci. Appl. 5(1), e16005 (2016).
[Crossref] [PubMed]

Sun, D. W.

J. H. Cheng, D. W. Sun, and H. Pu, “Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle,” Food Chem. 197(Pt A), 855–863 (2016).
[Crossref] [PubMed]

Tanaka, N.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102(4), 852–857 (2011).
[Crossref] [PubMed]

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

Thompson, K. P.

J. Reimers, A. Bauer, K. P. Thompson, and J. P. Rolland, “Freeform spectrometer enabling increased compactness,” Light Sci. Appl. 6(7), e17026 (2017).
[Crossref] [PubMed]

Uto, K.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102(4), 852–857 (2011).
[Crossref] [PubMed]

Vera, E.

Wallrabe, U.

S. Schuhladen, K. Banerjee, M. Stürmer, P. Müller, U. Wallrabe, and H. Zappe, “Variable optofluidic slit aperture,” Light Sci. Appl. 5(1), e16005 (2016).
[Crossref] [PubMed]

Wetzstein, G.

Wu, Y.

Yoon, T.

T. Yoon, C. S. Kim, K. Kim, and J. R. Choi, “Emerging applications of digital micromirror devices in biophotonic fields,” Opt. Laser Technol. 104, 17–25 (2018).
[Crossref]

Young, I. T.

Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: principles and applications,” Cytometry A 69(8), 735–747 (2006).
[Crossref] [PubMed]

Zappe, H.

S. Schuhladen, K. Banerjee, M. Stürmer, P. Müller, U. Wallrabe, and H. Zappe, “Variable optofluidic slit aperture,” Light Sci. Appl. 5(1), e16005 (2016).
[Crossref] [PubMed]

Zubik, M.

A. Siedliska, P. Baranowski, M. Zubik, W. Mazurek, and B. Sosnowska, “Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging,” Postharvest Biol. Technol. 139, 115–126 (2018).
[Crossref]

Appl. Opt. (1)

Cancer Sci. (1)

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102(4), 852–857 (2011).
[Crossref] [PubMed]

Cytometry A (1)

Y. Garini, I. T. Young, and G. McNamara, “Spectral imaging: principles and applications,” Cytometry A 69(8), 735–747 (2006).
[Crossref] [PubMed]

Food Chem. (1)

J. H. Cheng, D. W. Sun, and H. Pu, “Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle,” Food Chem. 197(Pt A), 855–863 (2016).
[Crossref] [PubMed]

IEEE Trans. Biomed. Eng. (1)

H. Akbari, Y. Kosugi, K. Kojima, and N. Tanaka, “Detection and analysis of the intestinal ischemia using visible and invisible hyperspectral imaging,” IEEE Trans. Biomed. Eng. 57(8), 2011–2017 (2010).
[Crossref] [PubMed]

J. Biomed. Opt. (1)

G. Lu and B. Fei, “Medical hyperspectral imaging: a review,” J. Biomed. Opt. 19(1), 10901 (2014).
[Crossref] [PubMed]

J. Food Eng. (1)

K. Kamruzzaman, Y. Makino, and S. Oshita, “Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning,” J. Food Eng. 170(7), 8–15 (2016).
[Crossref]

Light Sci. Appl. (2)

J. Reimers, A. Bauer, K. P. Thompson, and J. P. Rolland, “Freeform spectrometer enabling increased compactness,” Light Sci. Appl. 6(7), e17026 (2017).
[Crossref] [PubMed]

S. Schuhladen, K. Banerjee, M. Stürmer, P. Müller, U. Wallrabe, and H. Zappe, “Variable optofluidic slit aperture,” Light Sci. Appl. 5(1), e16005 (2016).
[Crossref] [PubMed]

Nat. Commun. (1)

W. Jahr, B. Schmid, C. Schmied, F. O. Fahrbach, and J. Huisken, “Hyperspectral light sheet microscopy,” Nat. Commun. 6(1), 7990 (2015).
[Crossref] [PubMed]

Opt. Eng. (2)

R. G. Sellar and G. D. Boreman, “Classification of imaging spectrometers for remote sensing applications,” Opt. Eng. 44(1), 013602 (2005).
[Crossref]

N. Hagen and M. Kudenov, “Review of snapshot spectral imaging technologies,” Opt. Eng. 52(9), 090901 (2013).
[Crossref]

Opt. Express (2)

Opt. Laser Technol. (1)

T. Yoon, C. S. Kim, K. Kim, and J. R. Choi, “Emerging applications of digital micromirror devices in biophotonic fields,” Opt. Laser Technol. 104, 17–25 (2018).
[Crossref]

Opt. Lett. (2)

Optica (1)

Postharvest Biol. Technol. (1)

A. Siedliska, P. Baranowski, M. Zubik, W. Mazurek, and B. Sosnowska, “Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging,” Postharvest Biol. Technol. 139, 115–126 (2018).
[Crossref]

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Figures (12)

Fig. 1
Fig. 1 Conceptual illustration of the DSSRT-HSI system.
Fig. 2
Fig. 2 The layout of the complete optical system and MTF.
Fig. 3
Fig. 3 Functional demonstration of the programmable control of spatially linear scanning of DMD. (a)–(c) The 2nd, 10th, and 19th modulation unit’s scanning with k = 50 and n = 21. (d)–(f) The 3rd, 26th, and 40th modulation unit’s scanning with k = 16 and n = 64. (g)–(i) The 15th, 106th, and 215th modulation unit’s scanning with k = 4 and n = 256.
Fig. 4
Fig. 4 Schematic procedure to acquire a spectral image of λ1. (a) The image of an object on the DMD. (b) Dispersive spectrum of seven channels collected by the CCD with the inset denoting the pixel fusion. (c) λ1 wavelength’s extraction. (d) The reconstructed spectral image of λ1.
Fig. 5
Fig. 5 (a) The prototype of the DSSRT-HSI system. (b) A leaf with diseases and pests. (c) Spectra comparison between the DSSRT-HSI system and Oceanview.
Fig. 6
Fig. 6 Relationship using regression linear equation of the normalized intensity measured by the DSSRT-HSI system with different k and Oceanview.
Fig. 7
Fig. 7 The 127th modulation unit’s fitting results of k = 4. (a) The spectrally dispersed images of the 7th and 11th filter. (b) Twelve filters’ spectral curves of the middle field by Gaussians fitting with the central wavelength of 453.6, 471.5, 500.6, 510.6, 534.3, 548.5, 564.8, 571.4, 610.3, 623.5, 638.4 and 651.3 nm and with the FWHM of 24.44, 16.15, 16.61, 17.36, 20.66, 24.30, 17.54, 15.72, 15.37, 35.05, 15.62 and 30.52 nm. (c) The dispersive curve fitted with an approximate linear function.
Fig. 8
Fig. 8 The leaf’s grayscale spectral images of twenty channels with SR = 5 nm and k = 8.
Fig. 9
Fig. 9 (a)–(b) The spectral images with SR = 5 nm and the central wavelength of 530 nm and 610 nm, respectively. (c)–(e) The enlarged grayscale and false color spectral images of the ROI with the same central wavelength 610 nm and SR = 5 nm but different modulation unit size k. (f)–(h) The curves of the intensity and the pixel position exacted from the spectral images of the ROI. The green and red lines correspond to the same positions concerned.
Fig. 10
Fig. 10 Spectral images of the same central wavelength 610nm with k = 4 and SR = 1 nm (a), SR = 5 nm (b), SR = 10 nm (c) and SR = 20nm (d), respectively.
Fig. 11
Fig. 11 The measured spectral data and fitted curves of the point R and G with k = 6 and SR = 0.2 nm (The regions nearby two peak intensities are highlighted with a green bar and a red bar, respectively).
Fig. 12
Fig. 12 The spectrum of a red LED (a) and a green one (b) measured by the DSSRT-HSI system with k = 4 and Oceanview, respectively.

Tables (1)

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Table 1 Optical performance of the DSSRT-HSI system

Equations (9)

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n = 1024 k
b = 13.68 k
d x = b m s m
d x = 65.1 k
d y = s m
p = p n p 1 + 1
S R = 0.2 p
t = 1024 17 k
S R p i x e l a c = Δ λ f N p i x e l a c

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