OSA's Digital Library

Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editor: Gregory W. Faris
  • Vol. 4, Iss. 5 — May. 5, 2009

Reconstruction of reflectance data using an interpolation technique

Farhad Moghareh Abed, Seyed Hossein Amirshahi, and Mohammad RezaMoghareh Abed  »View Author Affiliations


JOSA A, Vol. 26, Issue 3, pp. 613-624 (2009)
http://dx.doi.org/10.1364/JOSAA.26.000613


View Full Text Article

Enhanced HTML    Acrobat PDF (1434 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

A linear interpolation method is applied for reconstruction of reflectance spectra of Munsell as well as ColorChecker SG color chips from the corresponding colorimetric values under a given set of viewing conditions. Hence, different types of lookup tables (LUTs) have been created to connect the colorimetric and spectrophotometeric data as the source and destination spaces in this approach. To optimize the algorithm, different color spaces and light sources have been used to build different types of LUTs. The effects of applied color datasets as well as employed color spaces are investigated. Results of recovery are evaluated by the mean and the maximum color difference values under other sets of standard light sources. The mean and the maximum values of root mean square (RMS) error between the reconstructed and the actual spectra are also calculated. Since the speed of reflectance reconstruction is a key point in the LUT algorithm, the processing time spent for interpolation of spectral data has also been measured for each model. Finally, the performance of the suggested interpolation technique is compared with that of the common principal component analysis method. According to the results, using the CIEXYZ tristimulus values as a source space shows priority over the CIELAB color space. Besides, the colorimetric position of a desired sample is a key point that indicates the success of the approach. In fact, because of the nature of the interpolation technique, the colorimetric position of the desired samples should be located inside the color gamut of available samples in the dataset. The resultant spectra that have been reconstructed by this technique show considerable improvement in terms of RMS error between the actual and the reconstructed reflectance spectra as well as CIELAB color differences under the other light source in comparison with those obtained from the standard PCA technique.

© 2009 Optical Society of America

OCIS Codes
(300.6550) Spectroscopy : Spectroscopy, visible
(330.1690) Vision, color, and visual optics : Color
(330.1730) Vision, color, and visual optics : Colorimetry

ToC Category:
Vision, Color, and Visual Optics

History
Original Manuscript: May 6, 2008
Revised Manuscript: November 17, 2008
Manuscript Accepted: November 23, 2008
Published: February 23, 2009

Virtual Issues
Vol. 4, Iss. 5 Virtual Journal for Biomedical Optics

Citation
Farhad Moghareh Abed, Seyed Hossein Amirshahi, and Mohammad Reza Moghareh Abed, "Reconstruction of reflectance data using an interpolation technique," J. Opt. Soc. Am. A 26, 613-624 (2009)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=josaa-26-3-613


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. R. S. Berns, Billmeyer and Saltzman's Principles of Color Technology, 3rd ed. (Wiley, 2000).
  2. D. Dupont, “Study of the reconstruction of reflectance curves based on tristimulus values: comparison of methods of optimization,” Color Res. Appl. 27, 88-99 (2002). [CrossRef]
  3. N. Salamati and S. H. Amirshahi, “The comparison between PCA and simplex methods for reflectance recovery,” in Proceedings of AIC Interim Meeting on Color Science for Industry, Hangzhou, China (2007), pp. 149-152.
  4. S. Usui, S. Nakauchi, and M. Nakano, “Reconstruction of Munsell color space by a five-layer neural network,” J. Opt. Soc. Am. A 9, 516-520 (1992). [CrossRef]
  5. K. Ansari, S. H. Amirshahi, and S. Moradian, “Recovery of reflectance spectra from CIE tristimulus values using a progressive database selection technique,” J. Color. Technol. 122, 128-134 (2006). [CrossRef]
  6. C. J. Hawkyard, “Synthetic reflectance curves by subtractive color mixing,” J. Soc. Dyers Colour. 109, 246-251 (1993). [CrossRef]
  7. C. J. Hawkyard, “Synthetic reflectance curves by additive mixing,” J. Soc. Dyers Colour. 109, 323-329 (1993). [CrossRef]
  8. R. S. Berns and C. J. Hawkyard, “Synthetic reflectance curves,” J. Soc. Dyers Colour. 110, 386-389 (1994).
  9. G. Wang, C. Li, and M. R. Luo, “Improving the Hawkyard method for generating reflectance functions,” Color Res. Appl. 30, 283-287 (2005). [CrossRef]
  10. Y. Zhao and R. S. Berns, “Image-based spectral reflectance reconstruction using the matrix R method,” Color Res. Appl. 32, 343-351 (2007). [CrossRef]
  11. H. S. Fairman and M. H. Brill, “The principal components of reflectance,” Color Res. Appl. 29, 104-110 (2004). [CrossRef]
  12. F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, “Reconstruction of reflectance spectra using weighted principal component analysis,” Color Res. Appl. 33, 369-371 (2008). [CrossRef]
  13. F. Ayala, J. F. Echávarri, P. Renet, and A. I. Negueruela, “Use of three tristimulus values from surface reflectance spectra to calculate the principal components for reconstructing these spectra by using only three eigenvectors,” J. Opt. Soc. Am. A 23, 2020-2026 (2006). [CrossRef]
  14. N. Attarchi and S. H. Amirshahi, “Reconstruction of reflectance data by modification of Berns' Gaussian method,” Color Res. Appl. 34, 26-32 (2009). [CrossRef]
  15. A. Shams-Nateri, “Effect of a standard colorimetric observer on the reconstruction of reflectance spectra of coloured fabrics,” Coloration Technology 124, 14-18 (2008). [CrossRef]
  16. S. Zuffi and R. Schettini, “Reflectance function estimation from tristimulus values,” Proc. SPIE 5293, 222-231 (2003). [CrossRef]
  17. 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]
  18. M. de Berg, M. van Krefeld, M. Overmars, and O. Schwarzkopf, Computational Geometry: Algorithms and Applications, 2nd ed. (Springer, 2000).
  19. I. Amidror, “Scattered data interpolation methods for electronic imaging systems: A survey,” J. Electron. Imaging 11, 157-176 (2002). [CrossRef]
  20. P. Green and L. MacDonald, Color Engineering Achieving Device Independent Colour (Addison-Wesley, 2002).
  21. J. Kasson, W. Plouffe, and S. Nin, “A tetrahedral interpolation technique for color space conversion,” Proc. SPIE 1909, 127-138 (1993). [CrossRef]
  22. University of Joensuu Color Group, “Spectral Database,” http://spectral.joensuu.fi/.

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited