Color Correction for Digital Images Based on the Finite-Dimensional Linear-Model

We investigate the intrinsic color characteristics used in accomplishing color constancy. The surface reflectance of an object can be divided into two parts: interface reflectance and body reflectance. The body component is the physical basis for determining the surface color characteristics of objects, but it is influenced by geometrical factors in measurement. To eliminate the effect of illumination and geometrical factors we adopt the normalized deviation reflectance, which was suggested by Tominaga, to describe the body reflection characteristics. Then, according to a finite-dimensional linear model, the body reflection characteristics are expressed in terms of weighting coefficients of the constrained basis functions, so as to reduce its dimensionality and make it easy to use in color classification. We choose objects made of inhomogeneous dielectric material as samples and use a grating spectrometer to measure their surface spectral reflectance, from which the description of intrinsic color characteristics can be derived based on normalized deviation reflectance and a finite-dimensional linear model. The experimental results show that this description is very stable under different conditions and also compact, so that it can serve well for color classification.

We present a statistical technique to characterize the global color distribution in an image. The result can be used for color correction of a single image and for com- parison of different images. It is assumed that the object colors are similar to those in a set of colors for which spectral reflectances are available (in our experiments we use spectral measurements of the Munsell and NCS color chips). The logarithm of the spectra can be approximated by finite linear combinations of a small number of basis vec- tors. We characterize the distributions of the expansion coefficients in an image by their modes (the most probable values). This description does not require the assumption of a special class of probability distributions and it is insen- sitive to outliers and other perturbations of the distribu- tions. A change of illumination results in a global shift of the expansion coefficients and, thus, also their modes. The recovery of the illuminant is thus reduced to estimating these shift parameters. The calculated light distribution is only an estimate of the true spectral distribution of the illuminant. Direct inverse filtering for normalization may lead to undesirable results, since these processes are often ill-defined. Therefore, we apply regularization techniques in applications (such as automatic color correction) where visual appearance is important. We also demonstrate how to use this characterization of the global color distribution in an image as a tool in color-based search in image databases. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 98 -111, 1999

H2 approximation of multiple input/output delay systems

Color correction is an important methodology where a digital image’s colors undergo a transformation to more accurately represent their appearance using a predefined set of illumination conditions. Colorimetric measurements in diagnostics are sensitive to very small changes in colors and therefore require consistent, reproducible illumination conditions to produce accurate results, making color correction a necessity. This paper presents an image color correction pipeline developed by HueDx, Inc., using transfer algorithms that improve upon existing methodologies and demonstrates real-world applications of this pipeline in colorimetric clinical chemistry using a smartphone enabled, paper-based total protein diagnostic assay. Our pipeline is able to compensate for a variety of illumination conditions to provide consistent imaging for quantitative colorimetric measurements using white-balancing, multivariate gaussian distributions and histogram regression via dynamic, non-linear interpolating lookup tables. We empirically demonstrate that each point in the color correction pipeline provides a theoretical basis for achieving consistent and precise color correction. To show this, we measure color difference with deltaE (ΔE00), alongside quantifying performance of the HueDx color correction system, including the phone hardware, color sticker manufacturing quality and software correction capabilities. The results show that the HueDx color correction system is capable of restoring images to near-imperceptible levels of difference independent of their original illumination conditions including brightness and color temperature. Comparisons drawn from the paper-based total protein assay calibrated and quantified with and without using the HueDx color correction pipeline show that the coefficient of variation in precision testing is almost twice as high without color-correcting. Limits of blank, detection and quantitation were also higher without color-correction. Overall, we were able to demonstrate the HueDx platform improves reading and outcome of the total protein diagnostic assay and is useful for the development of smartphone-based quantitative colorimetric diagnostic assays for point-of-care testing.

We represent local spatial structure in a color image using feature matrices that are computed from an image region. Feature matrices contain significantly more information about local image structure than previous representations. Although feature matrices are useful for surface recognition, this representation depends on the spectral properties of the scene illumination. Using a finite dimensional linear model for surface spectral reflectance with the same number of parameters as the number of color bands, we show that illumination changes correspond to linear transformations of the feature matrices and that surface rotations correspond to circular shifts of the matrices. From these relationships we derive an algorithm for illumination and geometry invariant recognition of local surface structure. We demonstrate the algorithm with a series of experiments on images of real objects.

BackgroundIn clinical practice, surgeons differentiate color changes in repaired cartilage compared with surrounding intact cartilage, but cannot quantify these color changes. Objective assessments are required. A spectrocolorimeter was used to evaluate whether intact and repaired cartilage can be quantified.FindingsWe investigated the use of a spectrocolorimeter and the application of two color models (L* a* b* colorimetric system and spectral reflectance distribution) to describe and quantify articular cartilage. In this study, we measured the colors of intact and repaired cartilage after a microfracture. Histologically, the repaired cartilage was a mixture of fibrocartilage and hyaline cartilage. In the L* a* b* colorimetric system, the L* and a* values recovered to close to the values of intact cartilage, whereas the b* value decreased over time after the operation. Regarding the spectral reflectance distribution at 12 weeks after the operation, the repaired cartilage had a higher spectral reflectance ratio than intact cartilage between wavelengths of 400 to 470 nm.ConclusionThis study reports the first results regarding the relationship between spectrocolorimetric evaluation and the histological findings of repair cartilage after a microfracture. Our findings demonstrate the ability of spectrocolorimetric measurement to judge the repair cartilage after treatment on the basis of objective data such as the L*, a* and b* values and the SRP as a coincidence index of the spectral reflectance curve.

Предмет изучения. В статье предложены новые методы и средства цветовой коррекции web-камер в системах стереозрения для повышения качества их работы. Целью является сопоставительный анализ показателей качества известных методов коррекции цвета, разработка новой методики и рабочих алгоритмов совместной процедуры цветовой коррекции и ректификации фреймов видеопотока левой и правой камер. Задачи: Ставилась задача выполнить теоретический анализ показателей качества известных алгоритмов коррекции цвета, разработать новые рабочие алгоритмы, программные коды этих алгоритмов написать на языке Python c использованием необходимых функций OpenCV. Провести экспериментальные исследования этих алгоритмов. Оценить эффективность работы стереосистемы в лабораторных условиях, а достоверность полученных результатов проверить методами статистического анализа. Используемые методы: Сопоставительный анализ известных методов и алгоритмов путем статистического моделирования, синтез новых алгоритмов и оценка эффективности их работы путем проведения лабораторных натурных испытаний. Полученные результаты: проведен сопоставительный анализ эффективности работы известных методов цветовой коррекции камер стереосистемы, предложены новые более эффективные алгоритмы для решения этой задачи. Выводы. Научная новизна полученных результатов: предложены новые методы коррекции цветового баланса web-камер, используемых в стереоскопических системах технического зрения, отличающиеся высокой точностью цветовой коррекции и работающих в реальном масштабе времени с применением функций библиотеки OpenCV в программной среде Python.

The problem of colour correction of underwater images concerns not only surveyors, who primarily use images for photogrammetric purposes, but also archaeologists, marine biologists, and many other experts of different disciplines whose aim is to study objects and lifeforms underwater. For this reason, this study aims to address and quantify the impact of an ad-hoc underwater image restoration algorithm regarding image feature matching and 3D reconstruction of the scene. The datasets used for the purpose of this study consist of the original and colour corrected images captured in two different sites located in Cyprus with different depths and different water conditions. The images were colour corrected following a simple mathematical model that utilizes the scene's geometry while considering also the absorption and backscattering of light in order to restore the missing colour information.

A method, using an optimization scheme, has been developed for the interpretation of spectral albedo (or spectral reflectance) curves obtained from remotely sensed water color data. This method used a two-flow model of the radiation flow and solves for the albedo. Optimization fitting of predicted to observed reflectance data is performed by a quadratic interpolation method for the variables chlorophyll concentration and scattering coefficient. The technique is applied to airborne water color data obtained from Kawartha Lakes, Sargasso Sea, and Nova Scotia coast. The modeled spectral albedo curves are compared to those obtained experimentally, and the computed optimum water parameters are compared to ground truth values. It is shown that the backscattered spectral signal contains information that can be interpreted to give quantitative estimates of the chlorophyll concentration and turbidity in the waters studied.

Effect of tooth color on the accuracy of intraoral complete arch scanning under different light conditions using a zirconia restoration model

The properties of the same pigments in murals are affected by different concentrations and particle diameters, which cause the shape of the spectral reflectance data curve to vary, thus influencing the outcome of matching calculations. This paper proposes a spectral matching classification method of multi-state similar pigments based on feature differences. Fast principal component analysis (FPCA) was used to calculate the eigenvalue variance of pigment spectral reflectance, then applied to the original reflectance values for parameter characterization. We first projected the original spectral reflectance from the spectral space to the characteristic variance space to identify the spectral curve. Secondly, the relative distance between the eigenvalues in the eigen variance space is combined with the JS (Jensen-Shannon) divergence to express the difference between the two spectral distributions. The JS information divergence calculates the relative distance between the eigenvalues. Experimental results show that our classification method can be used to identify the spectral curves of the same pigment under different states. The value of the root means square error (RMSE) decreased by 12.0817, while the mean values of the mean absolute percentage error (MAPE) and R2 increased by 0.0965 and 0.2849, respectively. Compared with the traditional spectral matching algorithm, the recognition error was effectively reduced.

Spectral imaging can provide spectral information from which spectral radiance or reflectance can be recovered at each image pixel. Recovery algorithms lead to good spectral and colorimetric performance by directly transforming RGB digital counts to spectral reflectances, but his approach is sensitive to the size and composition of the training set. What we propose here is a supervised method to select the most appropriate samples from a training database to buld the transformation matrix relating digital counts to spectral reflectances. Thus, this approach is tested with real images.

To utilize the full potential of multispectral data acquired from aerial photographs/satellite imagery increased knowledge of the spectral reflectance characteristics of Landuse/Landcover features are required. Spectro-Radiometer was used to collect the spectral reflectance values in wavelength regions ranging from 0.48 to 0.96 u m, of some Landuse/Landcover features, in Roorkee and its surrounding areas. Spectral reflectance values, thus collected, were used to draw spectral reflectance curves of each feature separately and to determine the optimum wavelength regions for identifying each Landuse/Landcover feature. The wavelength regions, in which two dissimilar Landuse/Landcover features exhibit nearly same tonal variation in B&W aerial photographs/satellite imagery, were also determined from these curves.

The methods to analyze the color of dyed or printed materials utilizing computer image analyzing systems were studied. Spectral reflectance curves and tristimulus values of the samples could be obtained as two dimensional data from the image of the sample. The reflection image of the sample was filtered and spectrally divided with 20 types of interference monochromatic glass filters (20 nm intervals, 400 - 780 nm) to 20 divisions of monochromatic images. Each monochromatic image was recorded by video camera, digitalized, and stored on the computer memories. From the images on the memories, spectral reflectance curve and CIE-XYZ tristimulus values at any point of the sample could be obtained with high accuracy. The technique could be applied to the quantitative analysis of the colorant absorbed on a solid substrate. The image of the sample formed by monochromatic reflection was converted to that formed by K/S values by computer calculation using the Kubelka-Munk equation. Since an intensity at any point in the K/S image could be related to the density of colorant at the corresponding point of the sample, the integration of the intensities of K/S image over enfire surface represents the total amount of colorant on the sample. Some examples of the method were demonstrated.

A color constancy mechanism is proposed to perceive the image color from the strengths of three RGB responses of a color camera or the like, independent of the color of the light illuminating the object. The first step of the method is to use a finite-dimensional linear model to estimate the color signals. It is shown that any color signal can be characterized as a linear combination of four principal component basis functions. Once the color signals have been estimated the unknown illuminant can be determined by S. Tominaga and B.A. Wandell's (1989) estimation method, which is based on a dichromatic model. In color constancy, the knowledge of surface reflectance helps in determining the canonical color descriptors despite the variations in the spectral power distribution of the ambient light. It is shown that the estimate of surface reflectance is derived from both the estimated color signal and illuminant straightforwardly. The model of a combination of the estimations of the surface spectral reflectance and the color signals can be employed in designing a color constancy electronic video camera which indeed improves the shortcomings of the traditional video camcorders.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>