Separating Illumination and Surface Spectral from Multiple Color Signals

A number of methods have been proposed to separate a color signal into its components: illumination spectral power distribution and surface spectral reflectance. Most of these methods usually use a minimization technique from solely a single color signal, which works in theoretical framework but is not effective for real data. The reason is it lacks the constraints necessary to make the iteration converge into correct separation. To resolve this problem, we proposed a minimization technique that, unlike the existing methods, uses multiple color signals. In our implementation, we introduce three different approaches: first, color signals obtained from two different surface reflectance lit by an identical illumination spectral power distribution; second, color signal from an identical surface reflectance lit by different illumination spectral power distributions; and third, color signals from identical surface reflectance but with different types of reflection components (diffuse and specular reflectance) lit by identical illumination spectral power distribution. Using multiple color signals can improve the robustness of the estimation, since we can obtain more constraints in the input data. And the experimental results on real spectral show the effectiveness of our method. In addition, practically we implement our method to deal with color signals of a scene taken using interference variable filter. The purpose is to obtain surface spectral reflectance and illumination spectral power distribution under some illumination light source.