The measured light spectrum is the result of an illuminant interacting with a surface. The illuminant spectral power distribution multiplies the surface spectral reflectance function to form a color signal—the light spectrum that gives rise to our perception. Disambiguation of the two factors, illuminant and surface, is difficult without prior knowledge. Previously [IEEE Trans. Pattern Anal. Mach. Intell. 12, 966 (1990) ; J. Opt. Soc. Am. A 21, 1825 (2004) ], one approach to this problem applied a finite-dimensional basis function model to recover the separate illuminant and surface reflectance components that make up the color signal, using principal component bases for lights and for reflectances. We introduce the idea of making use of finite-dimensional models of logarithms of spectra for this problem. Recognizing that multiplications turn into additions in such a formulation, we can replace the original iterative method with a direct, analytic algorithm with no iteration, resulting in a speedup of several orders of magnitude. Moreover, in the new, logarithm-based approach, it is straightforward to further design new basis functions, for both illuminant and reflectance simultaneously, such that the initial basis function coefficients derived from the input color signal are optimally mapped onto separate coefficients that produce spectra that more closely approximate the illuminant and the surface reflectance for any given dimensionality. This is accomplished by using an extra bias correction step that maps the analytically determined basis function coefficients onto the optimal coefficient set, separately for lights and surfaces, for the training set. The analytic equation plus the bias correction is then used for unknown input color signals.
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