Synthesis of Reflectance Spectra Using Non-Context-Based Features

Document Type : Original Article


Department of Textile Engineering, Amirkabir University of Technology (Tehran Polytechnic), P.O. Box: 159163-14311, Tehran, Iran.


The non-context-based approach is used for the synthesis of spectral reflectance curves of objects with known CIEXYZ tristimulus values. The method introduces two sets of features, i.e., the standard color-matching functions normalised by their sum in each wavelength and a group of two sigmoidal and one Gaussian bases that approximately fit the first set. The assigned spectra for the desired tristimulus colorimetric values are calculated using an additive color-mixing approach. Results of different methodologies are numerically compared in terms of root mean squared error (RMSE), goodness fit coefficient (GFC), and CIELAB color difference values between the actual and synthesised spectra. It is found that the synthesised spectra by the suggested primaries better resemble the actual behaviours of spectral reflectances of natural and synthetic objects in comparison to using three Gaussian primaries. Compared to other context and non-context-based approaches to spectral reconstruction, the suggested method is faster and does not require iterative optimisation. 


Main Subjects

  1. Abed FM, Amirshahi SH, Abed MRM. Reconstruction of reflectance data using an interpolation technique. J Opt Soc.Am A. 2009; 26(3): 613-624.
  2. Hawkyard C. Synthetic reflectance curves by subtractive colour mixing. J Soc Dyers Colour. 1993; 109(7-8): 246-251.
  3. Hawkyard C. Synthetic reflectance curves by additive mixing. J Soc Dyers Colour. 1993; 109(10): 323-329.
  4. Sun Y, Fracchia FD, Calvert TW, Drew MS. Deriving spectra from colors and rendering light interference. IEEE Comput Graph. 1999; 19(4): 61-67.
  5. Amiri MM, Amirshahi SH. A step by step recovery of spectral data from colorimetric information. J Opt. 2015; 44(4): 373-383.
  6. Attarchi N, Amirshahi SH. Reconstruction of reflectance data by modification of Berns' Gaussian method. Color Res Appl. 2009; 34(1): 26-32.
  7. Dupont D. Study of the reconstruction of reflectance curves based on tristimulus values: comparison of methods of optimization. Color Res Appl. 2002; 27(2): 88-99.
  8. Berns RS. Synthetic reflectance curves. J Soc Dyers Colour. 1994; 110: 386-388.
  9. Babaei V, Amirshahi SH, Agahian F. Using weighted pseudo‐inverse method for reconstruction of reflectance spectra and analyzing the dataset in terms of normality. Color Res Appl. 2011; 36(4): 295-305.
  10. Amirshahi SH, Amirhahi SA. Adaptive nonnegative bases for reconstruction of spectral data from colorimetric information. Opt Rev. 2010; 17(6): 562-569.
  11. Agahian F, Amirshahi SA, Amirshahi SH. Reconstruction of reflectance spectra using weighted principal component analysis. Color Res Appl. 2008; 33(5): 360-371.
  12. Rezaei I, Mahbadi AA, Amirshahi SH. Utilizing support vector and kernel ridge regression methods in spectral reconstruction. Res Opt. 2023; 11: 100405.
  13. Ansari K, Amirshahi SH, Moradian S. Recovery of reflectance spectra from CIE tristimulus values using a progressive database selection technique. Color Technol. 2006; 122(3): 128-134.
  14. Wang G, Li C, Luo MR. Improving the Hawkyard method for generating reflectance functions. Color Res Appl. 2005; 30(4): 283-287.
  15. Hawkyard C. Synthetic reflectance curves. J Soc Dyers Colour. 1994; 110(11): 386-389.
  16. Nantomah K. On some properties of the sigmoid function. Asia Mathematika. 2019.
  17. Ribeiro MI. Gaussian probability density functions: Properties and error characterization. Institute for Systems and Robotics, Lisboa, Portugal. 2004.
  18. Finland UoE. Spectral Database. [Internet]. Available from: