Analysis of image noise in multispectral color acquisition

The design of a system for multispectral image capture will be influenced by the imaging application, such as image archiving, vision research, illuminant modification or improved (trichromatic) color reproduction. A key aspect of the system performance is the effect of noise, or error, when acquiring multiple color image records and processing of the data. This research provides an analysis that allows the prediction of the image-noise characteristics of systems for the capture of multispectral images. The effects of both detector noise and image processing quantization on the color information are considered, as is the correlation between the errors in the component signals. The above multivariate error-propagation analysis is then applied to an actual prototype system. Sources of image noise in both digital camera and image processing are related to colorimetric errors. Recommendations for detector characteristics and image processing for future systems are then discussed

Methods of spectral reflectance reconstruction for a sinarback 54 digital camera

There is an urgent need to build digital image databases with adequate colorimetric accuracy for museums, achieves and libraries. Traditional colorimetric imaging suffers from the possibilities of metameric problem, while spectral imaging can facilitate accurate tristimulus estimation and possibilities for spectral reconstruction of each pixel. Spectral image archives can be used to render accurate images both spectrally and colorimetrically to the original target for any illuminant and observer. The most convenient and practical capture system for spectral imaging combines a commercial trichromatic camera with two absorption filters to define image spectrally. Two images were taken for each target; so six-channel multichannel images were obtained. Three methods of spectral color reproduction were evaluated: pseudoinverse method, canonical correlation regression (CCR), and Matrix R method. The CCR method can obtain the highest spectral accuracy among these methods, just because it incorporates fifteen cross product terms in the simulation. The Matrix R method can reach the same spectral accuracy as the pseudoinverse method, and the spectral accuracy of both methods could be improved if they also use the same cross product terms. On the other hand, the Matrix R can achieve the best colorimetric accuracy for a certain combination of illuminant and observer. Thus, the Matrix R is a very promising method for achieving artwork images with sufficient spectral and colorimetric accuracy

Modeling and Halftoning for Multichannel Printers: A Spectral Approach

Printing has been has been the major communication medium for many centuries. In the last twenty years, multichannel printing has brought new opportunities and challenges. Beside of extended colour gamut of the multichannel printer, the opportunity was presented to use a multichannel printer for ‘spectral printing’. The aim of spectral printing is typically the same as for colour printing; that is, to match input signal with printing specific ink combinations. In order to control printers so that the combination or mixture of inks results in specific colour or spectra requires a spectral reflectance printer model that estimates reflectance spectra from nominal dot coverage. The printer models have one of the key roles in accurate communication of colour to the printed media. Accordingly, this has been one of the most active research areas in printing. The research direction was toward improvement of the model accuracy, model simplicity and toward minimal resources used by the model in terms of computational power and usage of material. The contribution of the work included in the thesis is also directed toward improvement of the printer models but for the multichannel printing. The thesis is focused primarily on improving existing spectral printer models and developing a new model. In addition, the aim was to develop and implement a multichannel halftoning method which should provide with high image quality. Therefore, the research goals of the thesis were: maximal accuracy of printer models, optimal resource usage and maximal image quality of halftoning and whole spectral reproduction system. Maximal colour accuracy of a model but with the least resources used is achieved by optimizing printer model calibration process. First, estimation of the physical and optical dot gain is performed with newly proposed method and model. Second, a custom training target is estimated using the proposed new method. These two proposed methods and one proposed model were at the same time the means of optimal resource usage, both in computational time and material. The third goal was satisfied with newly proposed halftoning method for multichannel printing. This method also satisfies the goal of optimal computational time but with maintaining high image quality. When applied in spectral reproduction workflow, this halftoning reduces noise induced in an inversion of the printer model. Finally, a case study was conducted on the practical use of multichannel printers and spectral reproduction workflow. In addition to a gamut comparison in colour space, it is shown that otherwise limited reach of spectral printing could potentially be used to simulate spectra and colour of textile fabrics

Evaluation and optimal design of spectral sensitivities for digital color imaging

The quality of an image captured by color imaging system primarily depends on three factors: sensor spectral sensitivity, illumination and scene. While illumination is very important to be known, the sensitivity characteristics is critical to the success of imaging applications, and is necessary to be optimally designed under practical constraints. The ultimate image quality is judged subjectively by human visual system. This dissertation addresses the evaluation and optimal design of spectral sensitivity functions for digital color imaging devices. Color imaging fundamentals and device characterization are discussed in the first place. For the evaluation of spectral sensitivity functions, this dissertation concentrates on the consideration of imaging noise characteristics. Both signal-independent and signal-dependent noises form an imaging noise model and noises will be propagated while signal is processed. A new colorimetric quality metric, unified measure of goodness (UMG), which addresses color accuracy and noise performance simultaneously, is introduced and compared with other available quality metrics. Through comparison, UMG is designated as a primary evaluation metric. On the optimal design of spectral sensitivity functions, three generic approaches, optimization through enumeration evaluation, optimization of parameterized functions, and optimization of additional channel, are analyzed in the case of the filter fabrication process is unknown. Otherwise a hierarchical design approach is introduced, which emphasizes the use of the primary metric but the initial optimization results are refined through the application of multiple secondary metrics. Finally the validity of UMG as a primary metric and the hierarchical approach are experimentally tested and verified

Integrating colour correction algorithms

Digital cameras sense colour different than the human visual system (HVS). Digital cameras sense colour using imaging sensor, whereas the HVS senses colour using the cone photoreceptors in our retina. Each digital camera model has its own device specific spectral sensitivity function. It is therefore necessary to convert the device specific colour responses of an imaging sensor to values that are related to the HVS. This process is typically referred to as colour correction, and it is common to the image processing pipeline across all cameras. In this thesis, we explore the topic of colour correction for digital cameras. Colour correction algorithms establish the mapping between device specific responses of the camera with HVS related colour responses. Colour correction algorithms typically need to be trained with datasets. During the training process, we adjust the parameters of the colour correction algorithm, in order to minimise the fitting error between the device specific responses and the corresponding HVS responses. In this thesis, we first show that the choice of the training dataset affects the performance of the colour correction algorithm. Then, we propose to circumvent this problem by considering a reflectance dataset as a set of samples of a much larger reflectance space. We approximate the convex closure of the reflectance dataset in the reflectance space using a hypercube. Finally we integrate over this hypercube in order to calculate a matrix for linear colour correction. By computing the linear colour correction matrix this way, we are able to fill in the gap within a reflectance dataset. We then expand upon the idea of reflectance space further, by allowing all possible reflectances. We explore an alternative formulation of Maximum Ignorance with Positivity (MIP) colour correction. Our alternative formulation allows us to develop a polynomial variant of the concept. Polynomial MIP colour correction is far more complex thant MIP colour correction in terms of formulation. Our contribution is theoretically interesting, however practically, it delivers poorer performance

Learning from one example in machine vision by sharing probability densities

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 125-130).Human beings exhibit rapid learning when presented with a small number of images of a new object. A person can identify an object under a wide variety of visual conditions after having seen only a single example of that object. This ability can be partly explained by the application of previously learned statistical knowledge to a new setting. This thesis presents an approach to acquiring knowledge in one setting and using it in another. Specifically, we develop probability densities over common image changes. Given a single image of a new object and a model of change learned from a different object, we form a model of the new object that can be used for synthesis, classification, and other visual tasks. We start by modeling spatial changes. We develop a framework for learning statistical knowledge of spatial transformations in one task and using that knowledge in a new task. By sharing a probability density over spatial transformations learned from a sample of handwritten letters, we develop a handwritten digit classifier that achieves 88.6% accuracy using only a single hand-picked training example from each class. The classification scheme includes a new algorithm, congealing, for the joint alignment of a set of images using an entropy minimization criterion. We investigate properties of this algorithm and compare it to other methods of addressing spatial variability in images. We illustrate its application to binary images, gray-scale images, and a set of 3-D neonatal magnetic resonance brain volumes.Next, we extend the method of change modeling from spatial transformations to color transformations. By measuring statistically common joint color changes of a scene in an office environment, and then applying standard statistical techniques such as principal components analysis, we develop a probabilistic model of color change. We show that these color changes, which we call color flows, can be shared effectively between certain types of scenes. That is, a probability density over color change developed by observing one scene can provide useful information about the variability of another scene. We demonstrate a variety of applications including image synthesis, image matching, and shadow detection.by Erik G. Miller.Ph.D

Neural Correlates of Conscious Perception. The Role of Primary Visual Cortex in Visual Awareness.

This study investigates which neural populations represent low-level dimensions of conscious perception. First, a general framework is presented that will allow the separation of different aspects of visual awareness. A set of six criteria is developed that allows one to assess whether a neural population could in principle represent a dimension of conscious perception. These criteria are then applied to previous studies on the neurophysiology and neuropsychology of conscious perception. In the following empirical section a study on the relationship between perceived contrast and activity in primary visual cortex is performed using a combination of EEG, MEG and psychophysics. Lateral masking was used to dissociate the physical and the perceived contrast of a target grating. Transient potentials and magnetic fields evoked by the flashed target gratings were recorded and compared to psychophysical judgements of perceived contrast. At all investigated contrast levels, the amplitudes of electrophysiological transients correlated better with perceived than with physical target contrast. This held especially for the late transient. Source localisation indicated that the transients in question are likely to originate in primary visual cortex. The study presented here is the first ever to study perceptual constancy by recording psychophysics and physiological responses synchronously. The results identify the activity of primary visual cortex as the most likely neural basis of perceived contrast

Calm Displays and Their Applications : Making Emissive Displays Mimic Reflective Surfaces Using Visual Psychophysics, Light Sensing and Colour Science

Ph. D. Thesis.Our environment is increasingly full of obtrusive display panels, which become illuminating surfaces when on, and void black rectangles when off. Some researchers argue that emissive displays are incompatible with Weiser and Seely Brown's vision of "calm technology", due to their inability to seamlessly blend into the background. Indeed, Mankoff has shown that for any ambient technology, the ability to move into the periphery is the most relevant factor in their usability. In this thesis, a background mode for displays is proposed based on the idea that displays can look like an ordinary piece of reflective paper showing the same content. The thesis consists of three main parts. In the first part (Chapter 4), human colour matching performance between an emissive display and reflective paper under chromatic lighting conditions is measured in a psychophysical experiment. We find that threshold discrimination ellipses vary with condition (16.0×6.0 ΔEab on average), with lower sensitivity to chroma than hue changes. Match distributions are bimodal for some conditions. In the second part (Chapter 5), an algorithm enabling emissive displays to look like reflective paper is described and evaluated, giving an average error of ΔEab = 10.2 between display and paper. A field study showed that paper-like displays are more acceptable in bedrooms and that people are more likely to keep them always on than normal displays. Finally, the third part (Chapter 6) concerns the development and four-week trial of a paper-like display application. Using the autobiographical design method, a system for sharing bedtime with a remote partner was developed. We see that once unobtrusive, display systems are desired for use even in spaces like bedrooms. Paper-like displays enable both emerging and existing devices to move into the periphery and become “invisible”, and therefore provide a new building block of calm technology that is not achievable using simple emissive displays