Study of spectral reflectance reconstruction based on regularization matrix R method
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In order to solve the ill-posed problem in the process of reconstructing the spectral reflectance of the traditional matrix R method, a regularization matrix R method was proposed in this paper. Through analyzing the ill-posed equation of matrix R to reconstruct the spectral reflectance, the Tikhonov regularization method was researched to restrict the ill-posed problem to solve the Moore-Penrose pseudo inverse matrix. The L-curve method was used to obtain the optimal regularization parameter by training samples data in order to effectively restrict the ill-posed situation which was caused by the equation solving of spectral reconstruction. The experimental results verified that the proposed regularization matrix R method had higher spectral and chromatic accuracy of reconstructed spectrum than traditional matrix R method. At the same time, the proposed regularization matrix R method achieved good performance for color reproduction of real mural in practical application.
KeywordsSpectral reflectance reconstruction Matrix R Tikhonov regularization
The work has been supported by the Youth Fund of National Natural Science Foundation, China (Grant Nos. 51404182, 61701388), the International Science and Technology Cooperation Project of the Science and Technology Department of Shaanxi Province, China (Grant No. 2017KW-036), the Special fund of the Education Department of Shaanxi Province, China (Grant No. 17JK0431), the Soft Science Project of Science and Technology Bureau of Xi’an, China [Grant No. 2016043SF/RK06(3)], the Science and Technology Project of Science and Technology Bureau of Xi’an Beilin District, China (Grant Nos. GX1605, GX1606), and the Youth Science and Technology Fund Project of Xi’an University of Architecture And Technology, China (Grant No. QN1628).
- 1.Ren, P.Y., Liao, N.F., Chai, B.H., Yang, W.P., Li, S.X.: Spectral reflectance recovery based on multispectral imaging. Opt. Technol. 31(3), 427–429 (2005)Google Scholar
- 2.Yang, W.P., Xu, N., Duan, J.J.: Application and development of multispectral imaging technology in color reproduction. J. Yunnan Univ. Natly. 18(3), 191–197 (2009)Google Scholar
- 3.Liu, Z., Wan, X.X., Huang, X.G., Liu, Q., Li, C.: The study on spectral reflectance reconstruction based on wideband multi-spectral acquisition system. Spectrosc. Spectr. Anal. 33(4), 1076–1081 (2014)Google Scholar
- 6.Wyszecki, G.: Valenzmetrische untersuchung des zusammenhanges wischen normaler und anomaler trichromasie. Die Farbe. 1953(2), 39–52 (1953)Google Scholar
- 10.Fairman, H.S.: Recommender terminology for matrix R and metamerism. Color Res. Appl. 16(5), 337–341 (1991)Google Scholar
- 14.Cohen, J.B.: Visual Color and Color Mixture: The Fundamental Color Space. University of Illinois Press, Urbana (2001)Google Scholar
- 16.Imai, F.H., Rosen, M.R., Berns, R.S.: Comparative study of metrics for spectral match quality. In: Proceedings of the 1st European Conference on Color Graphics, Imaging and Vision (CGIV 2002). Spring filed, MA, pP. 492–496 (2002)Google Scholar
- 20.Tikhonov, A.N., Arsenin, V.Y.: Solution of ill-posed problems. Math. Comput. 32(144), 491–491 (1977)Google Scholar
- 24.Li, N., Xu, Z., Zhao, H.J., Deng, K.W.: Improved support vector machines model based on multi-spectral parameters. Clust. Comput. 20(6), 1–10 (2017)Google Scholar