Generalized spectral decomposition: a theory and practice to spectral reconstruction

The generalized spectral decomposition (GSD) theorem is introduced, and the generalized fundamental stimulus and metameric black are analyzed to show how they convey the valuable features in terms of color information. The suggestion would be considered as the generalization of Cohen and Kappauf's matrix R theory and its later application in parameric correction by Fairman. The GSD theorem provides a modular model whose arguments can be elaborately set up for high-performance spectral recovery. It is also shown that the suggested methods for spectral decomposition and/or spectral reconstruction proposed by different researchers could be considered as special cases of GSD.