The ability to obtain colour images invariant to changes of illumination is called colour
constancy. An algorithm for colour constancy takes sensor responses - digital images
- as input, estimates the ambient light and returns a corrected image in which the illuminant
influence over the colours has been removed. In this thesis we investigate the
step of illuminant estimation for colour constancy and aim to extend the state of the art
in this field.
We first revisit the Minkowski Family Norm framework for illuminant estimation.
Because, of all the simple statistical approaches, it is the most general formulation and,
crucially, delivers the best results. This thesis makes four technical contributions. First,
we reformulate the Minkowski approach to provide better estimation when a constraint
on illumination is employed. Second, we show how the method can (by orders of
magnitude) be implemented to run much faster than previous algorithms. Third, we
show how a simple edge based variant delivers improved estimation compared with the
state of the art across many datasets. In contradistinction to the prior state of the art our
definition of edges is fixed (a simple combination of first and second derivatives) i.e.
we do not tune our algorithm to particular image datasets. This performance is further
improved by incorporating a gamut constraint on surface colour -our 4th contribution.
The thesis finishes by considering our approach in the context of a recent OSA
competition run to benchmark computational algorithms operating on physiologically
relevant cone based input data. Here we find that Constrained Minkowski Norms operi
ii
ating on spectrally sharpened cone sensors (linear combinations of the cones that behave
more like camera sensors) supports competition leading illuminant estimation
