Skip to main content
SpringerLink
Log in

Effect of the restoration of saturated signals in hyperspectral image analysis and color reproduction

  • Regular Paper
  • Published:
Optical Review Aims and scope Submit manuscript

Abstract

In hyperspectral imaging, the captured signal is often affected by saturation due to specular reflection or a peaky spectrum. In this paper, we propose a restoration method for saturated hyperspectral signals. Our algorithm is based on principal component analysis to obtain the reconstruction basis and then solve a linear constrained least square problem to calculate the coefficients of each basis. We discuss the problems that saturated signals might cause and apply our method to two sets of real hyperspectral images and a set of hyperspectral images with simulated saturation. The results show that our method helps increase unsupervised object detection and improves high-fidelity color reproduction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I.B.: Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 90(3), 337–352 (2004)

    Article  ADS  Google Scholar 

  2. Van der Meer, F.D., Van der Werff, H.M., Van Ruitenbeek, F.J., Hecker, C.A., Bakker, W.H., Noomen, M.F., Woldai, T.: Multi-and hyperspectral geologic remote sensing: a review. Int. J. Appl. Earth Obs. Geoinf. 14(1), 112–128 (2012)

    Article  Google Scholar 

  3. Dalponte, M., Bruzzone, L., Gianelle, D.: Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. IEEE Trans. Geosci. Remote Sens. 46(5), 1416–1427 (2008)

    Article  ADS  Google Scholar 

  4. Gono, K., Obi, T., Yamaguchi, M., Oyama, N., Machida, H., Sano, Y., Endo, T.: Appearance of enhanced tissue features in narrow-band endoscopic imaging. J. Biomed. Opt. 9(3), 568–578 (2004)

    Article  ADS  Google Scholar 

  5. Manolakis, D., Marden, D., Shaw, G.A.: Hyperspectral image processing for automatic target detection applications. Linc. Lab. J. 14(1), 79–116 (2003)

    Google Scholar 

  6. Murakami, Y., Nomura, J., Ohyama, M., Yamaguchi, M.: Fidelity evaluation of metallic luster in six-band high-dynamic-range imaging. Opt. Rev. 19(3), 142–149 (2012)

    Article  Google Scholar 

  7. Banterle, F., Artusi, A., Debattista, K., Chalmers, A.: Advanced High Dynamic Range Imaging, CRC press (2017)

  8. Brauers, J., Schulte, N., Bell, A.A., Aach, T.: Multispectral high dynamic range imaging. In Color Imaging XIII: processing, hardcopy, and applications (Vol. 6807, p. 680704). International Society for Optics and Photonics (2008)

  9. Hill, B., Vorhagen, F.W.: U.S. Patent No. 5,319,472. Washington, DC: U.S. Patent and Trademark Office (1994)

  10. Lapray, P.J., Wang, X., Thomas, J.B., Gouton, P.: Multispectral filter arrays: Recent advances and practical implementation. Sensors 14(11), 21626–21659 (2014)

    Article  Google Scholar 

  11. Gupta, R., Hartley, R.I.: Linear pushbroom cameras. IEEE Trans. Pattern Anal. Mach. Intell. 19(9), 963–975 (1997)

    Article  Google Scholar 

  12. Abel, J.S., Smith, J.O.: Restoring a clipped signal. In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing (pp. 1745–1748). IEEE (1991)

  13. Dahimene, A., Noureddine, M., Azrar, A.: A simple algorithm for the restoration of clipped speech signal. Informatica 32(2), 183–188 (2008)

    MATH  Google Scholar 

  14. Zhang, X., Brainard, D.H.: Estimation of saturated pixel values in digital color imaging. JOSA A 21(12), 2301–2310 (2004)

    Article  ADS  Google Scholar 

  15. Guo, D., Cheng, Y., Zhuo, S., Sim, T.: Correcting over-exposure in photographs. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 515–521). IEEE (2010)

  16. Zhang, H., He, W., Zhang, L., Shen, H., Yuan, Q.: Hyperspectral image restoration using low-rank matrix recovery. IEEE Trans. Geosci. Remote Sens. 52(8), 4729–4743 (2013)

    Article  ADS  Google Scholar 

  17. Liao, W., Goossens, B., Aelterman, J., Luong, H.Q., Pižurica, A., Wouters, N., Philips, W.: Hyperspectral image deblurring with PCA and total variation. In: 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) (pp. 1–4). IEEE. (2013)

  18. Tzeng, D.Y., Berns, R.S.: A review of principal component analysis and its applications to color technology. Color Res. Appl. 30(2), 84–98 (2005)

    Article  Google Scholar 

  19. Takara, Y., Manago, N., Saito, H., Mabuchi, Y., Kondoh, A., Fujimori, T., Kuze, H.: Remote sensing applications with NH hyperspectral portable video camera. In: Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV (Vol. 8527, p. 85271G). International Society for Optics and Photonics (2012)

  20. Reed, I.S., Yu, X.: Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans. Acoust. Speech Signal Process. 38(10), 1760–1770 (1990)

    Article  ADS  Google Scholar 

  21. Yan, L., Noro, N., Takara, Y., Ando, F., Yamaguchi, M.: Using hyperspectral image enhancement method for small size object detection on the sea surface. In: Image and Signal Processing for Remote Sensing XXI (Vol. 9643, p. 96430H). International Society for Optics and Photonics (2015)

  22. Nascimento, J.M., Dias, J.M.: Vertex component analysis: A fast algorithm to unmix hyperspectral data. IEEE Trans. Geosci. Remote Sens. 43(4), 898–910 (2005)

    Article  ADS  Google Scholar 

  23. Yamaguchi, M., Haneishi, H., Ohyama, N.: Beyond red–green–blue (RGB): spectrum-based color imaging technology. J. Imaging Sci. Technol. 52(1), 10201–10211 (2008)

    Article  Google Scholar 

  24. Haneishi, H., Hasegawa, T., Hosoi, A., Yokoyama, Y., Tsumura, N., Miyake, Y.: System design for accurately estimating the spectral reflectance of art paintings. Appl. Opt. 39(35), 6621–6632 (2000)

    Article  ADS  Google Scholar 

  25. Wu, D., Sun, D.W.: Colour measurements by computer vision for food quality control—a review. Trends Food Sci. Technol. 29(1), 5–20 (2013)

    Article  Google Scholar 

  26. Abe, T., Murakami, Y., Yamaguchi, M., Ohyama, N., Yagi, Y.: Color correction of pathological images based on dye amount quantification. Opt. Rev. 12(4), 293–300 (2005)

    Article  Google Scholar 

  27. Tominaga, S., Wandell, B.A.: Standard surface-reflectance model and illuminant estimation. JOSA A 6(4), 576–584 (1989)

    Article  ADS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Processing, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259-G2-28 Nagatsuta-cho, Midori-ku, Yokohama, 226-8503, Japan

    Lu Yan

  2. Department of Information and Communications Engineering, School of Engineering, Tokyo Institute of Technology, 4259-G2-28 Nagatsuta-cho, Midori-ku, Yokohama, 226-8503, Japan

    Masahiro Yamaguchi

  3. EBA JAPAN Co., Ltd., 2-17-12-301, Takanawa Minato-ku, Tokyo, 108-0074, Japan

    Naoki Noro, Yohei Takara & Fuminori Ando

Authors
  1. Lu Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masahiro Yamaguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Naoki Noro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yohei Takara
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fuminori Ando
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lu Yan.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, L., Yamaguchi, M., Noro, N. et al. Effect of the restoration of saturated signals in hyperspectral image analysis and color reproduction. Opt Rev 28, 27–41 (2021). https://doi.org/10.1007/s10043-020-00630-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10043-020-00630-8

Keywords

Search

Navigation