Illumination Estimation from Dichromatic Planes

Adopting the dichromatic reflection model under the assumption of neutral interface reflection, the color of the illuminating light can be estimated by intersecting the planes that the color response of two or more different materials describe. From the color response of any given region, most approaches estimate a single plane on the assumption that only a single material is imaged. This assumption, however, is often violated in cluttered scenes. In this paper, rather than a single planar model, several coexisting planes are used to explain the observed color response. In estimating the illuminant, a set of candidate lights is assessed for goodness of fit given the assumed number of coexisting planes. The candidate light giving the minimum error fit is then chosen as representative of the scene illuminant. The performance of the proposed approach is explored on real images

Revisiting Gray Pixel for Statistical Illumination Estimation

We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering. The method, called Mean Shifted Grey Pixel -- MSGP, is based on the observation: true-gray pixels are aligned towards one single direction. Our solution is compact, easy to compute and requires no training. Experiments on two real-world benchmarks show that the proposed approach outperforms state-of-the-art methods in the camera-agnostic scenario. In the setting where the camera is known, MSGP outperforms all statistical methods.Comment: updated and will appear in VISSAP 2019 (long paper

Color Constancy Adjustment using Sub-blocks of the Image

Extreme presence of the source light in digital images decreases the performance of many image processing algorithms, such as video analytics, object tracking and image segmentation. This paper presents a color constancy adjustment technique, which lessens the impact of large unvarying color areas of the image on the performance of the existing statistical based color correction algorithms. The proposed algorithm splits the input image into several non-overlapping blocks. It uses the Average Absolute Difference (AAD) value of each block’s color component as a measure to determine if the block has adequate color information to contribute to the color adjustment of the whole image. It is shown through experiments that by excluding the unvarying color areas of the image, the performances of the existing statistical-based color constancy methods are significantly improved. The experimental results of four benchmark image datasets validate that the proposed framework using Gray World, Max-RGB and Shades of Gray statistics-based methods’ images have significantly higher subjective and competitive objective color constancy than those of the existing and the state-of-the-art methods’ images

Extending minkowski norm illuminant estimation

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

Estimation of illuminants from color signals of illuminated objects

Color constancy is the ability of the human visual systems to discount the effect of the illumination and to assign approximate constant color descriptions to objects. This ability has long been studied and widely applied to many areas such as color reproduction and machine vision, especially with the development of digital color processing. This thesis work makes some improvements in illuminant estimation and computational color constancy based on the study and testing of existing algorithms. During recent years, it has been noticed that illuminant estimation based on gamut comparison is efficient and simple to implement. Although numerous investigations have been done in this field, there are still some deficiencies. A large part of this thesis has been work in the area of illuminant estimation through gamut comparison. Noting the importance of color lightness in gamut comparison, and also in order to simplify three-dimensional gamut calculation, a new illuminant estimation method is proposed through gamut comparison at separated lightness levels. Maximum color separation is a color constancy method which is based on the assumption that colors in a scene will obtain the largest gamut area under white illumination. The method was further derived and improved in this thesis to make it applicable and efficient. In addition, some intrinsic questions in gamut comparison methods, for example the relationship between the color space and the application of gamut or probability distribution, were investigated. Color constancy methods through spectral recovery have the limitation that there is no effective way to confine the range of object spectral reflectance. In this thesis, a new constraint on spectral reflectance based on the relative ratios of the parameters from principal component analysis (PCA) decomposition is proposed. The proposed constraint was applied to illuminant detection methods as a metric on the recovered spectral reflectance. Because of the importance of the sensor sensitivities and their wide variation, the influence from the sensor sensitivities on different kinds of illuminant estimation methods was also studied. Estimation method stability to wrong sensor information was tested, suggesting the possible solution to illuminant estimation on images with unknown sources. In addition, with the development of multi-channel imaging, some research on illuminant estimation for multi-channel images both on the correlated color temperature (CCT) estimation and the illuminant spectral recovery was performed in this thesis. All the improvement and new proposed methods in this thesis are tested and compared with those existing methods with best performance, both on synthetic data and real images. The comparison verified the high efficiency and implementation simplicity of the proposed methods