Bayesian method for recovering surface and illuminant properties from photosensor responses

The goal of computational color constancy is to recover the physical properties of illuminants and surfaces from photosensor responses. We formulate computational color constancy as a statistical estimation problem. We assume that the likelihood that any particular illuminant or surface will occur in a scene is governed by a prior probability distribution. In particular, we assume that illuminant spectral power distributions are drawn according to multivariate normal distribution over the weights of a finite dimensional linear model, and similarly for surface reflectance functions. Given a set of photosensor responses, Bayes rule may be applied to derive the posterior distribution for illuminants and surfaces. We discuss how to use the posterior to estimate the illuminant. We use simulation to compare the performance of a Bayesian algorithm to that of two previously reported color constancy algorithms. For our simulation conditions, the Bayesian algorithm results in the smallest expected estimation error.