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

Developing an imaging bi-spectrometer for fluorescent materials

Fluorescent effects have been observed for thousands of years. Stokes, in 1852, began the science of fluorescence culminating in his law of fluorescence, which explained that fluorescence emission occurs at longer wavelengths than the excitation wavelength. This phenomenon is observed extensively in the art world. Daylight fluorescent colors known as Day-Glo have become an artistic medium since the 1960s. Modern artists exploit these saturated and brilliant colors to glitter their painting. Multispectral imaging as a noninvasive technique has been used for archiving by museums and cultural-heritage institutions for about a decade. The complex fluorescence phenomenon has been often ignored in the multispectral projects. The ignored fluorescence results in errors in digital imaging of artwork containing fluorescent colors. The illuminant-dependency of the fluorescence radiance makes the fluorescence colorimetry and consequently spectral imaging more complex. In this dissertation an abridged imaging bi-spectrometer for artwork containing both fluorescent and non-fluorescent colors was developed. The method developed included two stages of reconstruction of the spectral reflected radiance factor and prediction of the fluorescent radiance factor. The estimation of the reflected radiance factor as a light source independent component was achieved by imaging with a series of short-wavelength cutoff filters placed in the illumination path. The fluorescent radiance factor, a light source dependent component, was estimated based on a proposed model, the abridged two-monochromator method. The abridged two-monochromator method was developed for reconstructing the bi-spectral matrix of a fluorescent color based on a calibrated UV-fluorescence imaging. In this way, one could predict the fluorescence radiance factor under any desired illuminant and consequently a better color evaluation and rendering could be obtained. Furthermore, this method easily fitted in a general system for spectral imaging of paintings containing both fluorescent and non-fluorescent colors. The abridged two-monochromator method could predict fluorescent radiance factor of a fluorescent color via prediction of the true emission and the number of absorbed quanta by a fluorescing specimen for a given viewing light source. The superiority of the abridged fluorescence spectral imaging to the traditional spectral and colorimetric imaging for a few light sources was confirmed using fluorescent and non-fluorescent targets. Additionally, an exploratory visual experiment using a paired-comparison method was performed to evaluate the performance of the abridged fluorescence spectral imaging in comparison to the traditional spectral and colorimetric imaging for rendering images of a reference painting. The abridged fluorescence spectral imaging had better performance than traditional spectral and colorimetric imaging in rendering images for daylight

Bootstrapping Color Constancy

Bootstrapping provides a novel approach to training a neural network to estimate the chromaticity of the illuminant in a scene given image data alone. For initial training, the network requires feedback about the accuracy of the network’s current results. In the case of a network for color constancy, this feedback is the chromaticity of the incident scene illumination. In the past1, perfect feedback has been used, but in the bootstrapping method feedback with a considerable degree of random error can be used to train the network instead. In particular, the grayworld algorithm2, which only provides modest color constancy performance, is used to train a neural network which in the end performs better than the grayworld algorithm used to train it

Preferred color correction for mixed taking-illuminant placement and cropping

The growth of automatic layout capabilities for publications such as photo books and image sharing websites enables consumers to create personalized presentations without much experience or the use of professional page design software. Automated color correction of images has been well studied over the years, but the methodology for determining how to correct images has almost exclusively considered images as independent indivisible objects. In modern documents, such as photo books or web sharing sites, images are automatically placed on pages in juxtaposition to others and some images are automatically cropped. Understanding how color correction preferences are impacted by complex arrangements has become important. A small number of photographs taken under a variety illumination conditions were presented to observers both individually and in combinations. Cropped and uncropped versions of the shots were included. Users had opportunities to set preferred color balance and chroma for the images within the experiment. Analyses point toward trends indicating a preference for higher chroma for most cropped images in comparison to settings for the full spatial extent images. It is also shown that observers make different color balance choices when correcting an image in isolation versus when correcting the same image in the presence of a second shot taken under a different illuminant. Across 84 responses, approximately 60% showed the tendency to choose image white points that were further from the display white point when multiple images from different taking illuminants were simultaneously presented versus when the images were adjusted in isolation on the same display. Observers were also shown to preserve the relative white point bias of the original taking illuminants

Color diversity index : the effect of chromatic adaptation

Common descriptors of light quality fail to predict the chromatic diversity produced by the same illuminant in different contexts. The aim of this paper was to study the influence of the chromatic adaptation in the context of the development of the color diversity index, a new index capable of predicting illuminant-induced variations in several types of images. The spectral reflectance obtained from hyperspectral images of natural, indoor and artistic paintings, and the spectral reflectance of 1264 Munsell surfaces were converted into the CIELAB color space for each of the 55 CIE illuminants and 5 light sources tested. The influence of the CAT02 chromatic adaptation was estimated for each illuminant and for each scene. The CIELAB volume was estimated by the convex hull method and the number of discernible colors was estimated by segmenting the CIELAB color volume into unitary cubes and by counting the number of non-empty cubes. High correlation was found between the CIELAB volume occupied by the Munsell surfaces and the number of discernible colors and the CILEAB color volume of the colors in all images analyzed. The effects of the chromatic adaptation were marginal and did not change the overall result. These results indicate that the efficiency of the new illuminant chromatic diversity index is not influenced by chromatic adaptation

Evaluation and improvement of the workflow of digital imaging of fine art reproduction in museums

Fine arts refer to a broad spectrum of art formats, ie~painting, calligraphy, photography, architecture, and so forth. Fine art reproductions are to create surrogates of the original artwork that are able to faithfully deliver the aesthetics and feelings of the original. Traditionally, reproductions of fine art are made in the form of catalogs, postcards or books by museums, libraries, archives, and so on (hereafter called museums for simplicity). With the widespread adoption of digital archiving in museums, more and more artwork is reproduced to be viewed on a display. For example, artwork collections are made available through museum websites and Google Art Project for art lovers to view on their own displays. In the thesis, we study the fine art reproduction of paintings in the form of soft copy viewed on displays by answering four questions: (1) what is the impact of the viewing condition and original on image quality evaluation? (2) can image quality be improved by avoiding visual editing in current workflows of fine art reproduction? (3) can lightweight spectral imaging be used for fine art reproduction? and (4) what is the performance of spectral reproductions compared with reproductions by current workflows? We started with evaluating the perceived image quality of fine art reproduction created by representative museums in the United States under controlled and uncontrolled environments with and without the presence of the original artwork. The experimental results suggest that the image quality is highly correlated with the color accuracy of the reproduction only when the original is present and the reproduction is evaluated on a characterized display. We then examined the workflows to create these reproductions, and found that current workflows rely heavily on visual editing and retouching (global and local color adjustments on the digital reproduction) to improve the color accuracy of the reproduction. Visual editing and retouching can be both time-consuming and subjective in nature (depending on experts\u27 own experience and understanding of the artwork) lowering the efficiency of artwork digitization considerably. We therefore propose to improve the workflow of fine art reproduction by (1) automating the process of visual editing and retouching in current workflows based on RGB acquisition systems and by (2) recovering the spectral reflectance of the painting with off-the-shelf equipment under commonly available lighting conditions. Finally, we studied the perceived image quality of reproductions created by current three-channel (RGB) workflows with those by spectral imaging and those based on an exemplar-based method