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

The Hyper-log-chromaticity space for illuminant invariance

Variation in illumination conditions through a scene is a common issue for classification, segmentation and recognition applications. Traffic monitoring and driver assistance systems have difficulty with the changing illumination conditions at night, throughout the day, with multiple sources (especially at night) and in the presence of shadows. The majority of existing algorithms for color constancy or shadow detection rely on multiple frames for comparison or to build a background model. The proposed approach uses a novel color space inspired by the Log-Chromaticity space and modifies the bilateral filter to equalize illumination across objects using a single frame. Neighboring pixels of the same color, but of different brightness, are assumed to be of the same object/material. The utility of the algorithm is studied over day and night simulated scenes of varying complexity. The objective is not to provide a product for visual inspection but rather an alternate image with fewer illumination related issues for other algorithms to process. The usefulness of the filter is demonstrated by applying two simple classifiers and comparing the class statistics. The hyper-log-chromaticity image and the filtered image both improve the quality of the classification relative to the un-processed image

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

Colour constancy in simple and complex scenes

PhD ThesisColour constancy is defined as the ability to perceive the surface colours of objects within scenes as approximately constant through changes in scene illumination. Colour constancy in real life functions so seamlessly that most people do not realise that the colour of the light emanating from an object can change markedly throughout the day. Constancy measurements made in simple scenes constructed from flat coloured patches do not produce constancy of this high degree. The question that must be asked is: what are the features of everyday scenes that improve constancy? A novel technique is presented for testing colour constancy. Results are presented showing measurements of constancy in simple and complex scenes. More specifically, matching experiments are performed for patches against uniform and multi-patch backgrounds, the latter of which provide colour contrast. Objects created by the addition of shape and 3-D shading information are also matched against backgrounds consisting of matte reflecting patches. In the final set of experiments observers match detailed depictions of objects - rich in chromatic contrast, shading, mutual illumination and other real life features - within depictions of real life scenes. The results show similar performance across the conditions that contain chromatic contrast, although some uncertainty still remains as to whether the results are indicative of human colour constancy performance or to sensory match capabilities. An interesting division exists between patch matches performed against uniform and multi-patch backgrounds that is manifested as a shift in CIE xy space. A simple model of early chromatic processes is proposed and examined in the context of the results

Linear color correction for multiple illumination changes and non-overlapping cameras

Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per-location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto-exposure algorithm found in the recent literature plus a colour correction one based on the inverse-intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state-of-the-art algorithms

Ridge Regression Approach to Color Constancy

This thesis presents the work on color constancy and its application in the field of computer vision. Color constancy is a phenomena of representing (visualizing) the reflectance properties of the scene independent of the illumination spectrum. The motivation behind this work is two folds:The primary motivation is to seek ‘consistency and stability’ in color reproduction and algorithm performance respectively because color is used as one of the important features in many computer vision applications; therefore consistency of the color features is essential for high application success. Second motivation is to reduce ‘computational complexity’ without sacrificing the primary motivation.This work presents machine learning approach to color constancy. An empirical model is developed from the training data. Neural network and support vector machine are two prominent nonlinear learning theories. The work on support vector machine based color constancy shows its superior performance over neural networks based color constancy in terms of stability. But support vector machine is time consuming method. Alternative approach to support vectormachine, is a simple, fast and analytically solvable linear modeling technique known as ‘Ridge regression’. It learns the dependency between the surface reflectance and illumination from a presented training sample of data. Ridge regression provides answer to the two fold motivation behind this work, i.e., stable and computationally simple approach. The proposed algorithms, ‘Support vector machine’ and ‘Ridge regression’ involves three step processes: First, an input matrix constructed from the preprocessed training data set is trained toobtain a trained model. Second, test images are presented to the trained model to obtain the chromaticity estimate of the illuminants present in the testing images. Finally, linear diagonal transformation is performed to obtain the color corrected image. The results show the effectiveness of the proposed algorithms on both calibrated and uncalibrated data set in comparison to the methods discussed in literature review. Finally, thesis concludes with a complete discussion and summary on comparison between the proposed approaches and other algorithms