In this paper we consider the problem of colour constancy; how given an image of a scene under an unknown illuminant can we recover an estimate of that light? We develop a general correlation framework in which solving for colour constancy is posed as a correlation of the colours
in an image with the colours that can occur under each of a set of possible lights. Rather than attempting to recover a single estimate of the illuminant as many previous authors have done, we, in the first instance, recover a correlation measure for each possible illuminant. We then select
an estimate of the scene illuminant based on these correlations.The work presented here follows from previously published  work. In this paper we extend that work by showing that the correlation framework is rich enough to allow many existing algorithms to be expressed within it. The
grey-world, maximum RGB, gamut mapping, and Maloney Wandell algorithms, perhaps the algorithms most widely cited in the literature, are presented in this correlation framework. This work together with work published elsewhere  shows that almost all published algorithms based on a Mondrian
world can be formulated in the framework presented here. Significantly, the correlation framework can be used to add value to existing algorithms. For example, some of the problems associated with the Maloney-Wandell algorithm can be removed.