Abstract

Multispectral constancy enables the illuminant invariant representation of multi-spectral data. This article proposes an experimental investigation of multispectral constancy through the use of multispectral camera as a spectrophotometer for the reconstruction of surface reflectance. Three images with varying illuminations are captured and the spectra of material surfaces is reconstructed. The acquired images are transformed into canonical representation through the use of diagonal transform and spectral adaptation transform. Experimental results show that use of multispectral constancy is beneficial for both filter-wheel and snapshot multi-spectral cameras. The proposed concept is robust to errors in illuminant estimation and is able to perform well with linear spectral reconstruction method. This work makes us one step closer to the use of multispectral imaging for computer vision.

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

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References

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2018 (3)

H. A. Khan, J.-B. Thomas, J. Y. Hardeberg, and O. Laligant, “Spectral adaptation transform for multispectral constancy,” J. Imaging Sci. Technol. 62, 1020504 (2018).
[Crossref]

H. A. Khan, S. Mihoubi, B. Mathon, J.-B. Thomas, and J. Y. Hardeberg, “Hytexila: High resolution visible and near infrared hyperspectral texture images,” Sensors 18(7), 2045 (2018).
[Crossref]

C. Ni, J. Jia, M. Howard, K. Hirakawa, and A. Sarangan, “Single-shot multispectral imager using spatially multiplexed fourier spectral filters,” J. Opt. Soc. Am. B 35, 1072–1079 (2018).
[Crossref]

2017 (1)

2016 (1)

J.-B. Thomas, P.-J. Lapray, P. Gouton, and C. Clerc, “Spectral characterization of a prototype SFA camera for joint visible and NIR acquisition,” Sensors 16, 993 (2016).
[Crossref]

2014 (2)

P.-J. Lapray, X. Wang, J.-B. Thomas, and P. Gouton, “Multispectral filter arrays: Recent advances and practical implementation,” Sensors 14, 21626–21659 (2014).
[Crossref] [PubMed]

R. Shrestha and J. Y. Hardeberg, “Spectrogenic imaging: A novel approach to multispectral imaging in an uncontrolled environment,” Opt. Express 22, 9123–9133 (2014).
[Crossref] [PubMed]

2011 (3)

2010 (1)

2008 (1)

J. Brauers, N. Schulte, and T. Aach, “Multispectral filter-wheel cameras: Geometric distortion model and compensation algorithms,” IEEE Transactions on Image Process. 17, 2368–2380 (2008).
[Crossref]

2007 (3)

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Transactions on Image Process. 16, 2207–2214 (2007).
[Crossref]

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color. Res. & Appl. 32, 352–360 (2007).
[Crossref]

H.-L. Shen, P.-Q. Cai, S.-J. Shao, and J. H. Xin, “Reflectance reconstruction for multispectral imaging by adaptive wiener estimation,” Opt. Express 15, 15545–15554 (2007).
[Crossref] [PubMed]

2004 (1)

J. Conde, H. Haneishi, M. Yamaguchi, N. Ohyama, and J. Baez, “Spectral reflectance estimation of ancient Mexican codices, multispectral images approach,” Revista Mexicana de Fisica 50, 484–489 (2004).

2002 (1)

K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color. Res. & Appl. 27, 147–151 (2002).
[Crossref]

2001 (3)

J. Hernández-Andrés, J. Romero, J. L. Nieves, and R. L. Lee, “Color and spectral analysis of daylight in southern europe,” J. Opt. Soc. Am. A 18, 1325–1335 (2001).
[Crossref]

D. Connah, S. Westland, and M. G. A. Thomson, “Recovering spectral information using digital camera systems,” Color. Technol. 117, 309–312 (2001).
[Crossref]

D. Connah, S. Westland, and M. G. A. Thomson, “Recovering spectral information using digital camera systems,” Color. Technol. 117, 309–312 (2001).
[Crossref]

1999 (1)

O. Bertr and C. Tallon-Baudry, “Oscillatory gamma activity in humans: a possible role for object representation,” Trends Cogn. Sci. 3, 151–162 (1999).
[Crossref]

1989 (1)

1986 (2)

Aach, T.

J. Brauers, N. Schulte, and T. Aach, “Multispectral filter-wheel cameras: Geometric distortion model and compensation algorithms,” IEEE Transactions on Image Process. 17, 2368–2380 (2008).
[Crossref]

J. Klein and T. Aach, “Multispectral filter wheel cameras: Modeling aberrations for filters in front of lens,” in Digital Photography VIII, vol. 8299 (International Society for Optics and Photonics, 2012), pp. 8299.

Amano, K.

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color. Res. & Appl. 32, 352–360 (2007).
[Crossref]

Baez, J.

J. Conde, H. Haneishi, M. Yamaguchi, N. Ohyama, and J. Baez, “Spectral reflectance estimation of ancient Mexican codices, multispectral images approach,” Revista Mexicana de Fisica 50, 484–489 (2004).

Barnard, K.

K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color. Res. & Appl. 27, 147–151 (2002).
[Crossref]

Berns, R.

F. Imai and R. Berns, “Spectral estimation using trichromatic digital cameras,” in International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, (1999), pp. 42–49.

Berns, R. S.

F. H. Imai and R. S. Berns, “Spectral estimation using trichromatic digital cameras,” in Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, (Chiba University Chiba, Japan, 1999), pp. 1–8.

Bertr, O.

O. Bertr and C. Tallon-Baudry, “Oscillatory gamma activity in humans: a possible role for object representation,” Trends Cogn. Sci. 3, 151–162 (1999).
[Crossref]

Brauers, J.

J. Brauers, N. Schulte, and T. Aach, “Multispectral filter-wheel cameras: Geometric distortion model and compensation algorithms,” IEEE Transactions on Image Process. 17, 2368–2380 (2008).
[Crossref]

Cai, P.-Q.

Clerc, C.

J.-B. Thomas, P.-J. Lapray, P. Gouton, and C. Clerc, “Spectral characterization of a prototype SFA camera for joint visible and NIR acquisition,” Sensors 16, 993 (2016).
[Crossref]

Coath, A.

K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color. Res. & Appl. 27, 147–151 (2002).
[Crossref]

Collins, S.

Conde, J.

J. Conde, H. Haneishi, M. Yamaguchi, N. Ohyama, and J. Baez, “Spectral reflectance estimation of ancient Mexican codices, multispectral images approach,” Revista Mexicana de Fisica 50, 484–489 (2004).

Connah, D.

D. Connah, S. Westland, and M. G. A. Thomson, “Recovering spectral information using digital camera systems,” Color. Technol. 117, 309–312 (2001).
[Crossref]

D. Connah, S. Westland, and M. G. A. Thomson, “Recovering spectral information using digital camera systems,” Color. Technol. 117, 309–312 (2001).
[Crossref]

D. Connah, J. Y. Hardeberg, and S. Westland, “Comparison of linear spectral reconstruction methods for multispectral imaging,” in International Conference on Image Processing, ICIP, vol. 3 (2004), pp. 1497–1500.

D’Zmura, M.

Ebner, M.

M. Ebner, Color Constancy (Wiley Publishing, 2007), 1st ed.

Foster, D. H.

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color. Res. & Appl. 32, 352–360 (2007).
[Crossref]

Funt, B.

K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color. Res. & Appl. 27, 147–151 (2002).
[Crossref]

Gevers, T.

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Transactions on Image Process. 16, 2207–2214 (2007).
[Crossref]

Gijsenij, A.

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Transactions on Image Process. 16, 2207–2214 (2007).
[Crossref]

Gouton, P.

J.-B. Thomas, P.-J. Lapray, P. Gouton, and C. Clerc, “Spectral characterization of a prototype SFA camera for joint visible and NIR acquisition,” Sensors 16, 993 (2016).
[Crossref]

P.-J. Lapray, X. Wang, J.-B. Thomas, and P. Gouton, “Multispectral filter arrays: Recent advances and practical implementation,” Sensors 14, 21626–21659 (2014).
[Crossref] [PubMed]

Green, P.

H. A. Khan and P. Green, “Color characterization methods for a multispectral camera,” in International Symposium on Electronic Imaging 2018: Color Imaging XXIII: Displaying, Processing, Hardcopy, and Applications, (IS&T, San Francisco, United States, 2018), pp. 221–1–221–8.

Habili, N.

A. Sohaib, N. Habili, and A. Robles-Kelly, “Automatic exposure control for multispectral cameras,” in IEEE International Conference on Image Processing, (2013), pp. 2043–2047.

Hallikainen, J.

Haneishi, H.

J. Conde, H. Haneishi, M. Yamaguchi, N. Ohyama, and J. Baez, “Spectral reflectance estimation of ancient Mexican codices, multispectral images approach,” Revista Mexicana de Fisica 50, 484–489 (2004).

Hardeberg, J. Y.

H. A. Khan, J.-B. Thomas, J. Y. Hardeberg, and O. Laligant, “Spectral adaptation transform for multispectral constancy,” J. Imaging Sci. Technol. 62, 1020504 (2018).
[Crossref]

H. A. Khan, S. Mihoubi, B. Mathon, J.-B. Thomas, and J. Y. Hardeberg, “Hytexila: High resolution visible and near infrared hyperspectral texture images,” Sensors 18(7), 2045 (2018).
[Crossref]

H. A. Khan, J.-B. Thomas, J. Y. Hardeberg, and O. Laligant, “Illuminant estimation in multispectral imaging,” J. Opt. Soc. Am. A 34, 1085–1098 (2017).
[Crossref]

R. Shrestha and J. Y. Hardeberg, “Spectrogenic imaging: A novel approach to multispectral imaging in an uncontrolled environment,” Opt. Express 22, 9123–9133 (2014).
[Crossref] [PubMed]

R. Shrestha, J. Y. Hardeberg, and R. Khan, “Spatial arrangement of color filter array for multispectral image acquisition,” Proc. SPIE 7875, 787503 (2011).

J. Y. Hardeberg, Acquisition and reproduction of color images: Colorimetric and multispectral approaches (Universal Publishers, 2001).

J. Y. Hardeberg and R. Shrestha, “Multispectral colour imaging: Time to move out of the lab?” in Mid-term meeting of the International Colour Association (AIC), (2015), pp. 28–32.

H. A. Khan, J. B. Thomas, and J. Y. Hardeberg, “Multispectral constancy based on spectral adaptation transform,” in 20th Scandinavian Conf. on Image Analysis, (2017), pp. 459–470.
[Crossref]

D. Connah, J. Y. Hardeberg, and S. Westland, “Comparison of linear spectral reconstruction methods for multispectral imaging,” in International Conference on Image Processing, ICIP, vol. 3 (2004), pp. 1497–1500.

Hernández-Andrés, J.

Hirakawa, K.

Howard, M.

Imai, F.

F. Imai and R. Berns, “Spectral estimation using trichromatic digital cameras,” in International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, (1999), pp. 42–49.

Imai, F. H.

F. H. Imai and R. S. Berns, “Spectral estimation using trichromatic digital cameras,” in Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, (Chiba University Chiba, Japan, 1999), pp. 1–8.

Jaaskelainen, T.

Jia, J.

Khan, H. A.

H. A. Khan, S. Mihoubi, B. Mathon, J.-B. Thomas, and J. Y. Hardeberg, “Hytexila: High resolution visible and near infrared hyperspectral texture images,” Sensors 18(7), 2045 (2018).
[Crossref]

H. A. Khan, J.-B. Thomas, J. Y. Hardeberg, and O. Laligant, “Spectral adaptation transform for multispectral constancy,” J. Imaging Sci. Technol. 62, 1020504 (2018).
[Crossref]

H. A. Khan, J.-B. Thomas, J. Y. Hardeberg, and O. Laligant, “Illuminant estimation in multispectral imaging,” J. Opt. Soc. Am. A 34, 1085–1098 (2017).
[Crossref]

H. A. Khan, J. B. Thomas, and J. Y. Hardeberg, “Multispectral constancy based on spectral adaptation transform,” in 20th Scandinavian Conf. on Image Analysis, (2017), pp. 459–470.
[Crossref]

H. A. Khan, “Multispectral constancy for illuminant invariant representation of multispectral images,” PhD thesis, Norwegian University of Science and Technology (2018).

H. A. Khan and P. Green, “Color characterization methods for a multispectral camera,” in International Symposium on Electronic Imaging 2018: Color Imaging XXIII: Displaying, Processing, Hardcopy, and Applications, (IS&T, San Francisco, United States, 2018), pp. 221–1–221–8.

Khan, R.

R. Shrestha, J. Y. Hardeberg, and R. Khan, “Spatial arrangement of color filter array for multispectral image acquisition,” Proc. SPIE 7875, 787503 (2011).

Klein, J.

J. Klein and T. Aach, “Multispectral filter wheel cameras: Modeling aberrations for filters in front of lens,” in Digital Photography VIII, vol. 8299 (International Society for Optics and Photonics, 2012), pp. 8299.

Laligant, O.

H. A. Khan, J.-B. Thomas, J. Y. Hardeberg, and O. Laligant, “Spectral adaptation transform for multispectral constancy,” J. Imaging Sci. Technol. 62, 1020504 (2018).
[Crossref]

H. A. Khan, J.-B. Thomas, J. Y. Hardeberg, and O. Laligant, “Illuminant estimation in multispectral imaging,” J. Opt. Soc. Am. A 34, 1085–1098 (2017).
[Crossref]

Lapray, P.-J.

J.-B. Thomas, P.-J. Lapray, P. Gouton, and C. Clerc, “Spectral characterization of a prototype SFA camera for joint visible and NIR acquisition,” Sensors 16, 993 (2016).
[Crossref]

P.-J. Lapray, X. Wang, J.-B. Thomas, and P. Gouton, “Multispectral filter arrays: Recent advances and practical implementation,” Sensors 14, 21626–21659 (2014).
[Crossref] [PubMed]

Lee, R. L.

Lennie, P.

Lu, G.

D. Zhang and G. Lu, “Evaluation of similarity measurement for image retrieval,” in International Conference on Neural Networks and Signal Processing, vol. 2 (2003), pp. 928–931.

Maloney, L. T.

Martin, L.

K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color. Res. & Appl. 27, 147–151 (2002).
[Crossref]

Mathon, B.

H. A. Khan, S. Mihoubi, B. Mathon, J.-B. Thomas, and J. Y. Hardeberg, “Hytexila: High resolution visible and near infrared hyperspectral texture images,” Sensors 18(7), 2045 (2018).
[Crossref]

Mihoubi, S.

H. A. Khan, S. Mihoubi, B. Mathon, J.-B. Thomas, and J. Y. Hardeberg, “Hytexila: High resolution visible and near infrared hyperspectral texture images,” Sensors 18(7), 2045 (2018).
[Crossref]

Nascimento, S. M. C.

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color. Res. & Appl. 32, 352–360 (2007).
[Crossref]

Ni, C.

Nieves, J. L.

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera and colored filters,” Color. Res. & Appl. 32, 352–360 (2007).
[Crossref]

J. Hernández-Andrés, J. Romero, J. L. Nieves, and R. L. Lee, “Color and spectral analysis of daylight in southern europe,” J. Opt. Soc. Am. A 18, 1325–1335 (2001).
[Crossref]

Ohyama, N.

J. Conde, H. Haneishi, M. Yamaguchi, N. Ohyama, and J. Baez, “Spectral reflectance estimation of ancient Mexican codices, multispectral images approach,” Revista Mexicana de Fisica 50, 484–489 (2004).

Parkkinen, J. P. S.

Ratnasingam, S.

Robles-Kelly, A.

A. Sohaib, N. Habili, and A. Robles-Kelly, “Automatic exposure control for multispectral cameras,” in IEEE International Conference on Image Processing, (2013), pp. 2043–2047.

Romero, J.

Sarangan, A.

Schulte, N.

J. Brauers, N. Schulte, and T. Aach, “Multispectral filter-wheel cameras: Geometric distortion model and compensation algorithms,” IEEE Transactions on Image Process. 17, 2368–2380 (2008).
[Crossref]

Shao, S.-J.

Shen, H.-L.

Shrestha, R.

R. Shrestha and J. Y. Hardeberg, “Spectrogenic imaging: A novel approach to multispectral imaging in an uncontrolled environment,” Opt. Express 22, 9123–9133 (2014).
[Crossref] [PubMed]

R. Shrestha, J. Y. Hardeberg, and R. Khan, “Spatial arrangement of color filter array for multispectral image acquisition,” Proc. SPIE 7875, 787503 (2011).

J. Y. Hardeberg and R. Shrestha, “Multispectral colour imaging: Time to move out of the lab?” in Mid-term meeting of the International Colour Association (AIC), (2015), pp. 28–32.

Sohaib, A.

A. Sohaib, N. Habili, and A. Robles-Kelly, “Automatic exposure control for multispectral cameras,” in IEEE International Conference on Image Processing, (2013), pp. 2043–2047.

Tallon-Baudry, C.

O. Bertr and C. Tallon-Baudry, “Oscillatory gamma activity in humans: a possible role for object representation,” Trends Cogn. Sci. 3, 151–162 (1999).
[Crossref]

Thomas, J. B.

H. A. Khan, J. B. Thomas, and J. Y. Hardeberg, “Multispectral constancy based on spectral adaptation transform,” in 20th Scandinavian Conf. on Image Analysis, (2017), pp. 459–470.
[Crossref]

Thomas, J.-B.

H. A. Khan, J.-B. Thomas, J. Y. Hardeberg, and O. Laligant, “Spectral adaptation transform for multispectral constancy,” J. Imaging Sci. Technol. 62, 1020504 (2018).
[Crossref]

H. A. Khan, S. Mihoubi, B. Mathon, J.-B. Thomas, and J. Y. Hardeberg, “Hytexila: High resolution visible and near infrared hyperspectral texture images,” Sensors 18(7), 2045 (2018).
[Crossref]

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Yamaguchi, M.

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Figures (5)

Fig. 1
Fig. 1 Color rendering of the scenes created in viewing booth and rendered under D65 illuminant.
Fig. 2
Fig. 2 Camera sensitivity (including the filters and sensor) in the visible range. X-axis show the wavelengths (in nm), while y-axis show the relative intensity. (Data taken from manufacturer).
Fig. 3
Fig. 3 Binary masks and the rendering of selected surfaces in color for the three images used in experiments. The whole scenes are shown in Fig. 1. Ground-truth for the selected patches is taken from hyperspectral dataset in [20].
Fig. 4
Fig. 4 Mean of RMSE from all the illuminants used in experiments.
Fig. 5
Fig. 5 Spectral reconstruction results from two surfaces along with measured (ground truth) spectrum. Results show that use of SAT along with diagonal transform causes reduction in the spectral reconstruction error.

Tables (6)

Tables Icon

Table 1 Spectral reconstruction result of selected surfaces from the “Kitchen” image. Each channel is acquired with manually adjusted integration time. Results are presented in form of mean and 95 percentile of error metric. Results of manually selecting the white patch of ColorChecker is shown with “Patch select”, while “SG-E” show results of Spectral gray-edge algo. with angular error (ΔA), after ColorChecker is masked out from image.

Tables Icon

Table 2 Spectral reconstruction result of selected surfaces from the “Kitchen” image, taken with simulation of snapshot camera. Results are presented in form of mean and 95 percentile of error metric. Results of manually selecting the white patch of ColorChecker is shown with “Patch select”, while “SG-E” show results of Spectral gray-edge algo. with angular error (ΔA), after ColorChecker is masked out from image.

Tables Icon

Table 3 Spectral reconstruction result of selected surfaces from the “Textile 1” image. Each channel is acquired with manually adjusted integration time. Results are presented in form of mean and 95 percentile of error metric. Results of manually selecting the white patch of ColorChecker is shown with “Patch select”, while “SG-E” show results of Spectral gray-edge algo. with angular error (ΔA), after ColorChecker is masked out from image. This scene is taken without illuminant A.

Tables Icon

Table 4 Spectral reconstruction result of selected surfaces from the “Textile 1” image, taken with simulation of snapshot camera. Results are presented in form of mean and 95 percentile of error metric. Results of manually selecting the white patch of ColorChecker is shown with “Patch select”, while “SG-E” show results of Spectral gray-edge algo. with angular error (ΔA), after ColorChecker is masked out from image.This scene is taken without illuminant A.

Tables Icon

Table 5 Spectral reconstruction result of selected surfaces from the “Textile 2” image. Each channel is acquired with manually adjusted integration time. Results are presented in form of mean and 95 percentile of error metric. Results of manually selecting the white patch of ColorChecker is shown with “Patch select”, while “SG-E” show results of Spectral gray-edge algo. with angular error (ΔA), after ColorChecker is masked out from image.

Tables Icon

Table 6 Spectral reconstruction result of selected surfaces from the “Textile_2” image, taken with simulation of snapshot camera. Results are presented in form of mean and 95 percentile of error metric. Results of manually selecting the white patch of ColorChecker is shown with “Patch select”, while “SG-E” show results of Spectral gray-edge algo. with angular error (ΔA), after ColorChecker is masked out from image.

Equations (13)

Equations on this page are rendered with MathJax. Learn more.

f = ω e ( λ ) r ( λ ) s ( λ ) d λ .
F = RES .
e = ω e ( λ ) s ( λ ) d λ .
F c = M c , ill F ill ,
D c , ill = diag ( E c / E ill ) .
F c = M c , ill F ill = A SAT D c , ill F ill .
R ^ = W c M c , ill F ill
( [ [ F σ ] p d x d x ) 1 / p = e ^ ,
W = R t R t T ( SE ) T ( ( SE ) R t R t T ( SE ) T + G ) 1 .
R ^ = W c A SAT D c , ill F ill .
RMSE = 1 N j = 1 N ( r j r ^ j ) 2
cosine distance = 1 r T r ^ ( r T r ) ( r ^ T r ^ )
Δ A = arccos e T e ^ ( e T e ) ( e ^ T e ^ )

Metrics