In this paper, we formulate the color constancy task as an image-to-image translation problem using GANs. By conducting a large set of experiments on different datasets, an experimental survey is provided on the use of different types of GANs to solve for color constancy i.e. CC-GANs (Color Constancy GANs). Based on the experimental review, recommendations are given for the design of CC-GAN architectures based on different criteria, circumstances and datasets.
To mimic the human ability of color constancy for cameras, a wide range of algorithms are proposed. These algorithms can be classified into two main categories: (1) static-based methods which are either relying on reflection models or low-level image statistics, and (2) learning-based methods which rely on training sets @cite_48 . Static methods assume that an image that is taken under a canonical light source follow certain properties. For example, the grey-world algorithm @cite_34 assumes that the average reflectance of the surfaces in a scene is achromatic. Further, the white-patch algorithm @cite_5 assumes that the maximum pixel response in an image corresponds to white reflectance. These assumptions are extended to first- and higher-order image statistics to provide more accurate estimators such as the grey-edge assumption @cite_16 . In their work, @cite_16 proves that all the statistics-based methods can be unified into a single framework.
@cite_48 @cite_5 @cite_34 @cite_16
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Computational color constancy is the task of estimating the true reflectances of visible surfaces in an image. In this paper we follow a line of research that assumes uniform illumination of a scene, and that the principal step in estimating reflectances is the estimation of the scene illuminant. We review recent approaches to illuminant estimation, firstly those based on formulae for normalisation of the reflectance distribution in an image - so-called grey-world algorithms, and those based on a Bayesian formulation of image formation. In evaluating these previous approaches we introduce a new tool in the form of a database of 568 high-quality, indoor and outdoor images, accurately labelled with illuminant, and preserved in their raw form, free of correction or normalisation. This has enabled us to establish several properties experimentally. Firstly automatic selection of grey-world algorithms according to image properties is not nearly so effective as has been thought. Secondly, it is shown that Bayesian illuminant estimation is significantly improved by the improved accuracy of priors for illuminant and reflectance that are obtained from the new dataset. We present a Bayesian approach to color constancy which utilizes a non-Gaussian probabilistic model of the image formation process. The parameters of this model are estimated directly from an uncalibrated image set and a small number of additional algorithmic parameters are chosen using cross validation. The algorithm is empirically shown to exhibit RMS error lower than other color constancy algorithms based on the Lambertian surface reflectance model when estimating the illuminants of a set of test images. This is demonstrated via a direct performance comparison utilizing a publicly available set of real world test images and code base. A method was recently devised for the recovery of an invariant image from a 3-band colour image. The invariant image, originally 1D greyscale but here derived as a 2D chromaticity, is independent of lighting, and also has shading removed: it forms an intrinsic image that may be used as a guide in recovering colour images that are independent of illumination conditions. Invariance to illuminant colour and intensity means that such images are free of shadows, as well, to a good degree. The method devised finds an intrinsic reflectivity image based on assumptions of Lambertian reflectance, approximately Planckian lighting, and fairly narrowband camera sensors. Nevertheless, the method works well when these assumptions do not hold. A crucial piece of information is the angle for an “invariant direction” in a log-chromaticity space. To date, we have gleaned this information via a preliminary calibration routine, using the camera involved to capture images of a colour target under different lights. In this paper, we show that we can in fact dispense with the calibration step, by recognizing a simple but important fact: the correct projection is that which minimizes entropy in the resulting invariant image. To show that this must be the case we first consider synthetic images, and then apply the method to real images. We show that not only does a correct shadow-free image emerge, but also that the angle found agrees with that recovered from a calibration. As a result, we can find shadow-free images for images with unknown camera, and the method is applied successfully to remove shadows from unsourced imagery. Statistics-based colour constancy algorithms work well as long as there are many colours in a scene, they fail however when the encountering scenes comprise few surfaces. In contrast, physics-based algorithms, based on an understanding of physical processes such as highlights and interreflections, are theoretically able to solve for colour constancy even when there are as few as two surfaces in a scene. Unfortunately, physics-based theories rarely work outside the lab. In this paper we show that a combination of physical and statistical knowledge leads to a surprisingly simple and powerful colour constancy algorithm, one that also works well for images of natural scenes. From a physical standpoint we observe that given the dichromatic model of image formation the colour signals coming from a single uniformly-coloured surface are mapped to a line in chromaticity space. One component of the line is defined by the colour of the illuminant (i.e. specular highlights) and the other is due to its matte, or Lambertian, reflectance. We then make the statistical observation that the chromaticities of common light sources all follow closely the Planckian locus of black-body radiators. It follows that by intersecting the dichromatic line with the Planckian locus we can estimate the chromaticity of the illumination. We can solve for colour constancy even when there is a single surface in the scene. When there are many surfaces in a scene the individual estimates from each surface are averaged together to improve accuracy. In a set of experiments on real images we show our approach delivers very good colour constancy. Moreover, performance is significantly better than previous dichromatic algorithms.