This paper is concerned with the derivation of a progression of shadow-free image representations. First, we show that adopting certain assumptions about lights and cameras leads to a 1D, gray-scale image representation which is illuminant invariant at each image pixel. We show that as a consequence, images represented in this form are shadow-free. We then extend this 1D representation to an equivalent 2D, chromaticity representation. We show that in this 2D representation, it is possible to relight all the image pixels in the same way, effectively deriving a 2D image representation which is additionally shadow-free. Finally, we show how to recover a 3D, full color shadow-free image representation by first (with the help of the 2D representation) identifying shadow edges. We then remove shadow edges from the edge-map of the original image by edge in-painting and we propose a method to reintegrate this thresholded edge map, thus deriving the sought-after 3D shadow-free image.

In this paper we propose that two images are captured of every scene: a normal image and an image captured where a coloured filter is placed in front of the camera. This additional information is then used in solving for colour constancy. The novelty of our approach is not that we add a colour filter (this is an old idea) but in how we use the additional information. In contradistinction to previous work we propose that the dimensionality of the 6 measurements per image pixel remains at 3 (not 6): we do not add a filter to increase the number of degrees of freedom but rather as a way of estimating the illuminant. We say that a filter is chromagenic if the relationship between filtered and unfiltered RGBs varies with and depends strongly on illumination. The canonical chromagenic algorithm works by testing the applicability of pre-computed relations in situ in an image. We extend the chromagenic approach to incorporate knowledge of the gamut of colours we expect to see under a given light and so in effect we make a hybrid gamut mapping + chromagenic algorithm. Experiments validate our approach with chromagenic gamut mapping shown to deliver significantly better constancy than all other algorithms tested.

Computational colour constancy tries to recover the colour of the scene illuminant of an image. Colour constancy algorithms can, in general, be divided into two groups: statistics-based approaches that exploit statistical knowledge of common lights and surfaces, and physics-based algorithms which are based on an understanding of how physical processes such as highlights manifest themselves in images. A combined physical and statistical colour constancy algorithm that integrates the advantages of the statistics-based colour by correlation method with those of a physics-based technique based on the dichromatic reflectance model is introduced. In contrast to other approaches not only a single illuminant estimate is provided but a set of likelihoods for a given illumination set. Experimental results on the benchmark Simon Fraser image database show the combined method to clearly outperform purely statistical and purely physical algorithms.

Gamut mapping colour constancy algorithms attempt to map image RGBs captured under an unknown light to corresponding RGBs under a reference light so as to render images colour constant. While the approach often works well, for a significant number of real images the algorithm delivers a null solution. We show that the null solution problem arises because of a failure of the diagonal model of illumination change on which the algorithm is based. We address the problem by proposing a new diagonal-offset model which has 6 rather than 3 parameters and which is able therefore to deal with a wider range of imaging conditions. Based on this model we formulate a convex programming solution to colour constancy and we show (by testing on real images) that the new algorithm is robust to the failures of the diagonal model and is capable of delivering very good colour constancy.

We present a database of full spatial and spectral resolution images of everyday objects captured with a novel multi-spectral device: the applied spectral imaging Spectracube camera. We summarise the operation of the device and present a study of its spectral accuracy before demonstrating the usefulness of the data set by using it to evaluate different white point correction transforms: transforms which map RGBs under one illuminant to a corresponding set of RGBs under a different light. We find that while the best transforms can often give results close to the results obtained using spectral techniques, the results are both image and illuminant dependent and in a number of cases all transforms tested introduce significant errors.

This paper presents a novel solution to the illuminant estimation problem: the problem of how, given an image of a scene taken under an unknown illuminant, we can recover an estimate of that light. The work is founded on previous gamut mapping solutions to the problem which solve for a scene illuminant by determining the set of diagonal mappings which take image data captured under an unknown light to a gamut of reference colours taken under a known light. Unfortunately a diagonal model is not always a valid model of illumination change and so previous approaches sometimes return a null solution. In addition, previous methods are difficult to implement. We address these problems by recasting the problem as one of illuminant classification: we define a priori a set of plausible lights thus ensuring that a scene illuminant estimate will always be found. A plausible light is represented by the gamut of colours observable under it and the illuminant in an image is classified by determining the plausible light whose gamut is most consistent with the image data. We show that this step (the main computational burden of the algorithm) can be performed simply, quickly, and efficiently by means of a non-negative least-squares optimisation. We report results on a large set of real images which show that it provides excellent illuminant estimation, outperforming previous algorithms.

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.

This paper addresses the question of what can be said about the colours in images that is independent of illumination. We make two main assumptions: Firstly, the illumination can be characterised as Planckian (a realistic assumption for most real scenes). Secondly, the camera behaves as if it were equipped with narrow band sensors (true for a large number of cameras). The resulting physics-based method results in a transformation of the original colour image to a grey-scale one which does not vary with illumination. We give results showing invariance under a range of illumination conditions.

The color constancy problem, that is, estimating the color of the scene illuminant from a set of image data recorded under an unknown light, is an important problem in computer vision and digital photography. The gamut mapping approach to color constancy is, to date, one of the most successful solutions to this problem. In this algorithm the set of mappings taking the image colors recorded under an unknown illuminant to the gamut of all colors observed under a standard illuminant is characterized. Then, at a second stage, a single mapping is selected from this feasible set. In the first version of this algorithm Forsyth (1990) mapped sensor values recorded under one illuminant to those recorded under a second, using a three-dimensional (3-D) diagonal matrix. However because the intensity of the scene illuminant cannot be recovered Finlayson (see IEEE Trans. Pattern Anal. Machine Intell. vol.18, no.10, p.1034-38, 1996) modified Forsyth's algorithm to work in a two-dimensional (2-D) chromaticity space and set out to recover only 2-D chromaticity mappings. While the chromaticity mapping overcomes the intensity problem it is not clear that something has not been lost in the process. The first result of this paper is to show that only intensity information is lost. Formally, we prove that the feasible set calculated by Forsyth's original algorithm, projected into 2-D, is the same as the feasible set calculated by the 2-D algorithm. Thus, there is no advantage in using the 3-D algorithm and we can use the simpler, 2-D version of the algorithm to characterize the set of feasible illuminants. Another problem with the chromaticity mapping is that it is perspective in nature and so chromaticities and chromaticity maps are perspectively distorted. Previous work demonstrated that the effects of perspective distortion were serious for the 2-D algorithm. Indeed, in order to select a sensible single mapping from the feasible set this set must first be mapped back up to 3-D. We extend this work to the case where a constraint on the possible color of the illuminant is factored into the gamut mapping algorithm. We show here that the illumination constraint can be enforced during selection without explicitly intersecting the two constraint sets. In the final part of this paper we reappraise the selection task. Gamut mapping returns the set of feasible illuminant maps. Our new algorithm is tested using real and synthetic images. The results of these tests show that the algorithm presented delivers excellent color 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.

Gamut mapping colour constancy attempts to determine the set of diagonal matrices taking the gamut of image colours under an unknown illuminant into the gamut of colours observed under a standard illuminant. Forsyth (1990) developed such an algorithm in rgb sensor space which Finlayson (1996) later modified to work in a 2-d chromaticity space. In this paper we prove that Forsyth's 3-d solution gamut is, when projected to 2-d, identical to the gamut recovered by the 2-d algorithm. Whilst this implies that there is no inherent disadvantage in working in chromaticity space, this algorithm has a number of problems; the 2-d solution set is distorted and contains practically non-feasible illuminants. These problems have been addressed separately in previous works; we address them together in this paper. Non-feasible illuminants are discarded by intersecting the solution gamut with a non-convex gamut of common illuminants. In 2-d this intersection is relatively simple, but to remove the distortion, both these sets should be represented as 3-d cones of mappings, and the intersection is more difficult. We present an algorithm which avoids performing this intersection explicitly and which is simple to implement. Tests of this algorithm on both real and synthetic images show that it performs significantly better than the best current algorithms.