Reflectance spectra recovery from tristimulus values by extraction of color feature match
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A procedure for recovering spectral reflectance from CIE tristimulus values is presented using a modified pseudo-inverse method. Unlike previous spectral recovery methods, this approach uses a new sample selection criterion based on color feature match to select a series of suitable samples for creating the adapted transformation matrix to reconstruct spectra reflectance. Taking into account the computational time and accuracy, the dynamic subgroups were created by dividing the spectral reflectance preliminarily, and the adapted subsets were created by the sample similarity/dissimilarity between samples in the dynamic subgroup and target sample. Consequently, instead of applying only one transformation matrix for the reconstruction process, a series of adapted transformation matrices were obtained from the adapted subsets using color feature match. Three different datasets of spectral reflectance and three different error metrics have been applied in this study. According to all the error metrics considered, the proposed method is quite accurate and outperforms the Pseudo-Inverse method and the weighted Pseudo-Inverse method, which is effective in reconstructing spectral reflectance.
KeywordsReflectance Spectrum reconstruction Pseudo-inverse estimation Color feature match
This study is supported by the National Natural Science Foundation of China (No. 61301231), Shanghai Young Teachers' Training Program (no.ZZslg15090) and the Innovation Fund Project for Graduate Student of Shanghai (No. JWCXSL1401).
- Ayala, F., Echávarri, J.F., Renet, P., Negueruela, A.I.: 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(8), 2020–2026 (2006). doi: 10.1364/JOSAA.23.002020 CrossRefADSGoogle Scholar
- García-Beltrán, A., Nieves, J.L., Hernández-Andrés, J., Romero, J.: Linear bases for spectral reflectance functions of acrylic paints. Color Res. Appl. 23(1), 39–45 (1998). doi: 10.1002/(SICI)1520-6378(199802)23:1<39:AID-COL6>3.0.CO;2-4 CrossRefGoogle Scholar
- Kruse, F.A., Lefkoff, A.B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J., Goetz, A.F.H.: The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44(2–3), 145–163 (1993). doi: 10.1016/0034-4257(93)90013-N CrossRefGoogle Scholar
- Spectral Database: University of Eastern Finland. http://www2.uef.fi/fi/spectral/spectral-database