Separating a Color Signal into Illumination and Surface Reflectance Components: Theory and Applications

We present a “separation” algorithm for achieving color constancy and theorems concerning its accuracy. The algorithm requires extra information from the optical system, over and above the usual three values mapping human cone responses. However, with this additional information-specifically, a sampling across the visible range of the reflected, color-signal spectrum impinging on the optical sensor-we are able to separate the illumination spectrum from the surface reflectance spectrum contained in the color-signal spectrum which is, of course, the product of these two spectra. At the heart of the separation algorithm is a general statistical method for finding the “best” illumination and reflectance spectra, within a space represented by finite-dimensional linear models of statistically typical spectra, whose product closely corresponds to the spectrum of the actual color signal. Using this method, we are able to increase the dimensionality of the finite-dimensional linear model for surfaces to a realistic value. One method of generating the spectral samples required for the separation algorithm is to use the chromatic aberration effects of a lens, and an example of this is given. We detail the accuracy achieved in a large range of tests and show that agreement with actual surface reflectance is excellent. Zndex Terms-Color constancy, color perception, computer vision, finite-dimensional linear model, statistical methods.