Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Automated detection of effective scene illuminant chromaticity from specular highlights in digital images

An advanced, automated method is presented for determining an effective scene illuminant chromaticity (scene illuminant plus imaging system variables) from specular highlights in digital images subsequent to image capture. Underlying theories are presented based on a two component reflection model where the scene illuminant relative spectral power distribution is preserved in the specular component. Related methodologies for extracting scene illuminant information as well as alternative methods for achieving color constancy are presented along with factors which inhibit successful implementation. Following, development of a more robust algorithm is discussed. This algorithm is based on locating the center of convergence of a radial line pattern in the two-dimensional chromaticity histogram which theoretically identifies the effective scene illuminant chromaticity. This is achieved by using a radiality index to quantify the relative correlation between a radial mask and the histogram radial line pattern at discrete chromaticity coordinates within a specified search region. The coordinates associated with the strongest radiality index are adopted to represent the effective scene illuminant chromaticity. For a set of controlled test images, the physics-based specular highlight algorithm determined effective scene illuminant chromaticities to a level of accuracy which was nearly three times better than that of a benchmark statistically-based gray-world algorithm. The primary advantage of the specular highlight algorithm was its sustained performance when presented with image conditions of dominant colors, weak specular reflections, and strong interreflections

Algorithms for the enhancement of dynamic range and colour constancy of digital images & video

One of the main objectives in digital imaging is to mimic the capabilities of the human eye, and perhaps, go beyond in certain aspects. However, the human visual system is so versatile, complex, and only partially understood that no up-to-date imaging technology has been able to accurately reproduce the capabilities of the it. The extraordinary capabilities of the human eye have become a crucial shortcoming in digital imaging, since digital photography, video recording, and computer vision applications have continued to demand more realistic and accurate imaging reproduction and analytic capabilities. Over decades, researchers have tried to solve the colour constancy problem, as well as extending the dynamic range of digital imaging devices by proposing a number of algorithms and instrumentation approaches. Nevertheless, no unique solution has been identified; this is partially due to the wide range of computer vision applications that require colour constancy and high dynamic range imaging, and the complexity of the human visual system to achieve effective colour constancy and dynamic range capabilities. The aim of the research presented in this thesis is to enhance the overall image quality within an image signal processor of digital cameras by achieving colour constancy and extending dynamic range capabilities. This is achieved by developing a set of advanced image-processing algorithms that are robust to a number of practical challenges and feasible to be implemented within an image signal processor used in consumer electronics imaging devises. The experiments conducted in this research show that the proposed algorithms supersede state-of-the-art methods in the fields of dynamic range and colour constancy. Moreover, this unique set of image processing algorithms show that if they are used within an image signal processor, they enable digital camera devices to mimic the human visual system s dynamic range and colour constancy capabilities; the ultimate goal of any state-of-the-art technique, or commercial imaging device

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

Camera Sensor Invariant Auto White Balance Algorithm Weighting

Color constancy is the ability to perceive colors of objects, invariant to the color of the light source. The aim for color constancy algorithms is first to estimate the illuminant of the light source, and then correct the image so that the corrected image appears to be taken under a canonical light source. The task of the automatic white balance (AWB) is to do the same in digital cameras so that the images taken by a digital camera look as natural as possible. The main challenge rises due to the illposed nature of the problem, that is both the spectral distribution of the illuminant and the scene reflectance are unknown. Most common methods used for addressing the AWB problem are based on low-level statistics assuming that illuminant information can be extracted from the image’s spatial information. Nevertheless, in recent studies the problem has been approached with machine learning techniques quite often and they have been proved to be very useful. In this thesis, we investigate learning color constancy using artificial neural networks (ANNs). Two different artificial neural network approaches are utilized to generate a new AWB algorithm by weighting some of the existing AWB algorithms. The first approach proves to be better than the existing approaches in terms of median error. On the other hand, the second method, which is better also from system design point of view, is superior to others including the first approach in terms of mean and median error. Furthermore, we also analyze camera sensor invariance by quantifying how much the performance of the ANNs degrade when the test sensor is different than the training sensor