Skip to main content
Book cover

International Workshop on Computational Color Imaging

CCIW 2015: Computational Color Imaging pp 23–32Cite as

Optics and Computational Methods for Hybrid Resolution Spectral Imaging

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9016)

Abstract

The concept and computational methods for hybrid resolution spectral imaging (HRSI) are introduced. In HRSI, a high-resolution spectral image is reconstructed with combining a high-resolution RGB image and a low-resolution spectral image. An important difficulty in high-resolution spectral imaging is that the light-energy is reduced at the image sensor. Such problem can be solved by the hybrid resolution approach, since the image resolution and quality are mostly determined by the high-resolution RGB image, which can be captured by commercial high-performance cameras. Different reconstruction methods suitable for a hybrid resolution system are reviewed and the performance of those methods is discussed. The hybrid resolution spectral video system is also demonstrated.

Keywords

  • Spectral imaging
  • Multispectral imaging
  • Hybrid resolution
  • Color reproduction
  • Low-resolution spectral sensor
  • Piecewise Wiener
  • Regression

Download conference paper PDF

References

  1. Anuta, P.E., MacDonald, R.B.: Crop surveys from multiband satellite photography using digital techniques. Remote Sensing of Environment 2, 53–67 (1971)

    CrossRef  Google Scholar 

  2. Hyvarinen, T., Herrala, E., Dall Ava, A.: Direct sight imaging spectrograph: A unique add-on component brings spectral imaging to industrial applications. Proc. SPIE 3302, 165–175 (1998)

    CrossRef  Google Scholar 

  3. Matsuoka, H., Kosai, Y., Saito, M., Takeyama, N., Suto, H.: Single-cell viability assessment with a novel spectro-imaging system. J. Biotechnol. 94, 299–308 (2002)

    CrossRef  Google Scholar 

  4. Levenson, R., Cronin, P.J., Pankratov, K.K.: Spectral imaging for brightfield microscopy. Proc. SPIE 4959, 27–33 (2003)

    CrossRef  Google Scholar 

  5. Yamaguchi, M., Murakami, Y., Hashizume, H., Haneishi, H., Kanno, Y., Komiya, Y.: High-fidelity color video reproduction of open surgery by six-band camera. Proc. SPIE 7627, 762707 (2010)

    Google Scholar 

  6. Hill, B., Vorhagen, F.W.: Multispectral image pick-up system. US Patent 5, 319, 472 (1994)

    Google Scholar 

  7. Burns, P.D., Berns, R.S.: Analysis of multispectral image capture. In: Proc. IS&T/SID 4th Color Imaging Conference, pp. 19–22 (1996)

    Google Scholar 

  8. Tominaga, S.: Multichannel vision system for estimating surface and illumination functions. J. Opt. Soc. Am. A 13, 2163–2173 (1996)

    CrossRef  Google Scholar 

  9. Yamaguchi, M., Haneishi, H., Ohyama, N.: Beyond red-green-blue (RGB): spectrum-based color imaging technology. J. Imaging Sci. Technol. 52, 010201 (2008)

    CrossRef  Google Scholar 

  10. Everitt, J.H., Escobar, D.E., Cavazos, I., Noriega, J.R., Davis, M.R.: A three-camera multispectral digital video imaging system. Remote Sensing of Environment 54(3), 333–337 (1995)

    CrossRef  Google Scholar 

  11. Ohsawa, K., Ajito, T., Fukuda, H., Komiya, Y., Haneishi, H., Yamaguchi, M., Ohyama, N.: Six-band HDTV camera system for spectrum-based color reproduction. J. Imaging Sci. Technol. 48, 85–92 (2004)

    Google Scholar 

  12. Leitner, R., De Biasio, M., Arnold, T., Dinh, C.V., Loog, M., Duin, R.P.W.: Multi-spectral video endoscopy system for the detection of cancerous tissue. Pattern Recognition Letters 34(1), 85–93 (2013)

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  14. Okamoto, T., Yamaguchi, I.: Simultaneous acquisition of spectral image information. Opt. Lett. 16, 1277–1279 (1991)

    CrossRef  Google Scholar 

  15. Descour, M.R., Volin, C.E., Dereniak, E.L., Thome, K.J., Schumacher, A.B., Wilson, D.W., Maker, P.D.: Demonstration of a high-speed nonscanning imaging spectrometer. Opt. Lett. 22, 1271–1273 (1997)

    CrossRef  Google Scholar 

  16. Wagadarikar, A.A., Pitsianis, N.P., Sun, X., Brady, D.J.: Video rate spectral imaging using a coded aperture snapshot spectral imager. Optics Express 17(8), 6368–6388 (2009)

    CrossRef  Google Scholar 

  17. Gao, L., Kester, R.T., Nagen, N., Tkaczyk, T.S.: Snapshot image mapping spectrometer (IMS) with high sampling density for hyperspectral microscopy. Opt. Express 18, 14330–14344 (2010)

    CrossRef  Google Scholar 

  18. Price, J.C.: Combining panchromatic and multispectral imagery from dual resolution satellite instruments. Remote Sens. Environ. 21, 119–128 (1987)

    CrossRef  Google Scholar 

  19. Eismann, M.T., Hardie, R.C.: Application of the stochastic mixing model to hyperspectral resolution enhancement. IEEE Trans. Image Process. 42, 1924–1933 (2004)

    Google Scholar 

  20. Murakami, Y., Ietomi, K., Yamaguchi, M., Ohyama, N.: Maximum a posteriori estimation of spectral reflectance from color image and multipoint spectral measurements. Appl. Opt. 46(28), 7068–7082 (2007)

    CrossRef  Google Scholar 

  21. Murakami, Y., Yamaguchi, M., Ohyama, N.: Piecewise Wiener estimation for reconstruction of spectral reflectance image by multipoint spectral measurements. Appl. Opt. 48(11), 2188–2202 (2009)

    CrossRef  Google Scholar 

  22. Murakami, Y., Yamaguchi, M., Ohyama, N.: Class-based spectral reconstruction based on unmixing of low-resolution spectral information. JOSA A 28(7), 1470–1481 (2011)

    CrossRef  Google Scholar 

  23. Murakami, Y., Yamaguchi, M., Ohyama, N.: Hybrid-resolution multispectral imaging using color filter array. Opt. Express 20(7), 7173–7183 (2012)

    CrossRef  Google Scholar 

  24. Murakami, Y., Nakazaki, K., Yamaguchi, M.: Hybrid-resolution spectral video system using low-resolution spectral sensor. Opt. Express 22(17), 20311–20325 (2014)

    CrossRef  Google Scholar 

  25. Nakazaki, K., Murakami, Y., Yamaguchi, M.: Hybrid-Resolution spectral imaging system using adaptive regression-based reconstruction. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2014. LNCS, vol. 8509, pp. 142–150. Springer, Heidelberg (2014)

    Google Scholar 

  26. Cao, X., Tong, X., Dai, Q., Lin, S.: High resolution multispectral video capture with a hybrid camera system. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, pp. 297–304 (2011)

    Google Scholar 

  27. Ma, C., Cao, X., Wu, R., Dai, Q.: Content-adaptive high-resolution hyperspectral video acquisition with a hybrid camera system. Opt. Lett. 39, 937–940 (2014)

    CrossRef  Google Scholar 

  28. Maloney, L.T.: Evaluation of linear models of surface spectral reflectance with small numbers of parameters. J. Opt. Soc. Am. A 3(10), 1673–1683 (1986)

    CrossRef  Google Scholar 

  29. Pratt, W.K., Mancill, C.E.: Spectral estimation techniques for the spectral calibration of a color image scanner. Appl. Opt. 15, 73–75 (1976)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Global Scientific Information and Computing Center, Tokyo Institute of Technology, Tokyo, Japan

    Masahiro Yamaguchi

Authors
  1. Masahiro Yamaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masahiro Yamaguchi .

Editor information

Editors and Affiliations

  1. University Jean Monnet, Saint-Etienne, France

    Alain Trémeau

  2. Dipartimento di Informatica Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Milano, Italy

    Raimondo Schettini

  3. Chiba University, Chiba, Japan

    Shoji Tominaga

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Yamaguchi, M. (2015). Optics and Computational Methods for Hybrid Resolution Spectral Imaging. In: Trémeau, A., Schettini, R., Tominaga, S. (eds) Computational Color Imaging. CCIW 2015. Lecture Notes in Computer Science(), vol 9016. Springer, Cham. https://doi.org/10.1007/978-3-319-15979-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-15979-9_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15978-2

  • Online ISBN: 978-3-319-15979-9

  • eBook Packages: Computer ScienceComputer Science (R0)