Paul M. Hubel

Color by correlation: a simple, unifying framework for color constancy

The paper considers the problem of illuminant estimation: how, given an image of a scene, recorded under an unknown light, we can recover an estimate of that light. Obtaining such an estimate is a central part of solving the color constancy problem. Thus, the work presented will have applications in fields such as color-based object recognition and digital photography. Rather than attempting to recover a single estimate of the illuminant, we instead set out to recover a measure of the likelihood that each of a set of possible illuminants was the scene illuminant. We begin by determining which image colors can occur (and how these colors are distributed) under each of a set of possible lights. We discuss how, for a given camera, we can obtain this knowledge. We then correlate this information with the colors in a particular image to obtain a measure of the likelihood that each of the possible lights was the scene illuminant. Finally, we use this likelihood information to choose a single light as an estimate of the scene illuminant. Computation is expressed and performed in a generic correlation framework which we develop. We propose a new probabilistic instantiation of this correlation framework and show that it delivers very good color constancy on both synthetic and real images. We further show that the proposed framework is rich enough to allow many existing algorithms to be expressed within it: the gray-world and gamut-mapping algorithms are presented in this framework and we also explore the relationship of these algorithms to other probabilistic and neural network approaches to color constancy.

Colour by correlation: a simple, unifying approach to colour constancy

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? Rather than recovering a single estimate of the illuminant as many previous authors have done, in the first instance we recover a measure of the likelihood that each possible illuminant was the scene illuminant. We do this by correlating image colours with the colours that can occur under each of a set of possible lights. We then recover an estimate of the scene illuminant based on these likelihoods. Computation is expressed and performed in a generic correlation framework which we develop in this paper. We develop a new probabilistic instantiation of this framework which delivers very good colour constancy on synthetic and real images. We show that the proposed framework is rich enough to allow many existing algorithms to be expressed within it; e.g. the grey-world and gamut mapping algorithms. We explore too the relationship of these algorithms to other probabilistic and neural network approaches.

Sharp transformations for color appearance

In this paper we describe the benefit of using a sharp transformation in the context of a color appearance model. The proposed scheme is shown to perform better than other models for the limited set of conditions tested. The testing method is similar to that described by Braun and Fairchild involving paired comparisons between prints under different illumination conditions and images calculated by the models for rendering on a CRT. Our testing shows that using a model that employs spectral sharpening for illuminant color compensation achieves better results than previous methods.