Luminance underestimates the brightness of chromatic visual stimuli. This phenomenon, known as the Helmholtz-Kohlrausch effect, is due to the different experimental methods—heterochromatic flicker photometry (luminance) and direct brightness matching (brightness)—from which these measures are derived. This paper probes the relationship between luminance and brightness through a psychophysical experiment that uses slowly oscillating visual stimuli and compares the results of such an experiment to the results of flicker photometry and direct brightness matching. The results show that the dimension of our internal color space corresponding with our achromatic response to stimuli is not a scale of brightness or lightness.
Spectral sensitivities represent the spectral property of a digital camera. Most of the prior art spectral sensitivities estimation algorithms were applied to reflective colour charts, while some algorithms used a LED-based target. In this study, the spectral sensitivities of camera were estimated from both the LED-based and reflective colour targets. Four algorithms including Tikhonov Regularization based on Derivatives, Fourier basis function, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), were implemented. The estimated accuracy was compared between different types of colour targets, and between different algorithms. It was found that the optimal algorithm was different when using LED-based and reflective colour targets.
The goals of this work are to accumulate the experimental data on contrast sensitivity functions and to establish a visual model that incorporates spatial frequency dependence. In addition, the experimental results from fixed-size and fixed-cycles stimuli and from different luminance levels were compared. Such a model is highly desired for applications that rely on image quality and to serve the lighting and imaging industries. The detection thresholds have been measured for chromatic contrast patterns at different spatial frequencies. The experimental parameters included: (1) four colour centres (white, green, yellow, and red), which were recommended by the International Commission on Illumination (CIE), at two different luminance levels for each colour centre; (2) three colour directions for each colour centre, namely luminance, red-green and yellow-blue; (3) five spatial frequencies, 0.06, 0.24, 0.96, 3.84, and 6.00 cycles per degree (cpd) for fixed-cycles stimulation, in which two spatial frequencies, 0.24 and 6.00 cpd, were also chosen for fixed-size stimuli. In this experiment, a 10-bit display characterized by GOG model was used to obtain contrast thresholds of different colour centres by 2-alternative forced choice method and stair-case method. The experimental results revealed different parameter effects (colour centres, luminance, colour direction, fixed cycle/size), and also supported McCann's conclusion that the number of cycles affects the comparative sensitivity. Most importantly, a cone contrast model was successfully developed by fitting the visual test data (fixed number of cycles). The model could accurately predict the contrast sensitivity of different color centers, spatial frequencies and stimulus.
Colors, generally, have effects on human interpretations that can manifest in a variety of emotions, reactions, and behaviors. The objective of this study is to understand the reasoning behind the choices of pill colors in relation to expected efficacy of drugs, as well as the color associations made by participants. The research was conducted at several university campuses in USA, UAE, Croatia, Kosovo, and China, and focused on different age brackets, gender, ethnic backgrounds, educational levels, and pill usage frequency. Understanding the reasoning and color associations helps us better comprehend the expected efficacy of drugs, and can therefore support pharmaceutical companies in designing and manufacturing drugs, thereby maximizing the potential effect on patients’ adherence rates.
Illuminant estimation is critically important in computational color constancy, which has attracted great attentions and motivated the development of various statistical- and learning-based methods. Past studies, however, seldom investigated the performance of the methods on pure color images (i.e., an image that is dominated by a single pure color), which are actually very common in daily life. In this paper, we develop a lightweight feature-based Deep Neural Network (DNN) model—Pure Color Constancy (PCC). The model uses four color features (i.e., chromaticity of the maximal, mean, the brightest, and darkest pixels) as the inputs and only contains less than 0.5k parameters. It only takes 0.25ms for processing an image and has good cross-sensor performance. The angular errors on three standard datasets are generally comparable to the state-of-the-art methods. More importantly, the model results in significantly smaller angular errors on the pure color images in PolyU Pure Color dataset, which was recently collected by us.
Materials with special appearance properties such as goniochromatic materials require complex bidirectional measurements to properly characterise their colour and gloss. Normally, these measurements are performed by goniospectrophotometers which are expensive and not commonly available. In this paper a flexible imaging system composed of a snapshot multispectral camera and a light source attached to a robotic arm, is used to obtain HDR BRDF measurements of patinas commonly used in cultural heritage objects. The system is evaluated by comparing the results to those of a commercially available goniospectrophotometer. It is found that with a known uncertainty, the system is capable of producing accurate measurements of samples with a roughness equal or lower than 6.19 μm. For roughnesses higher that 12.48 μm, the accuracy of the system decreases. Moreover, it is found that the size and orientation of the region of interest plays a great influence on the precision of the imaging system.
Various uniform color spaces and color appearance models were mainly developed for characterizing stimuli under a low dynamic range condition. Real scenes in daily life, however, are commonly high dynamic range (HDR), containing highlights with luminance beyond the diffuse white, whose color appearance characterization was never investigated in the past. This study was carefully designed to investigate the color appearance characterization of highlights in HDR scenes, covering extremely wide ranges of diffuse white luminance (up to 11000 cd/m2), stimulus luminance (up to 49000 cd/m2), stimulus chromaticities (reach Rec. 2020 gamut), and scene luminance contrast (up to 72045). The observers viewed two stimuli, including one highlight and one dark stimulus, in a viewing booth, and were asked to adjust the color appearance of another stimulus, so that the color differences between the adjusted stimulus to each of the other two stimuli appeared the same. The results clearly showed that none of the existing models, including the one (i.e., ICtCp) that was recently designed for HDR scenes, has a good performance. The models using a power function to characterize the non-linear compressive responses of the human visual system (i.e., CIELAB and IPT) had a slightly better performance. The findings provided some guidance for performing tone mapping and chroma/saturation adjustments, and clearly suggest the necessity to carry out further work to develop a better model for HDR scenes.
Many image-editing tasks are carried out in the gradient domain. Suppose that for an image I the gradient ∇I consists of a pair of fields (p;q); then some image “reintegration” scheme is tasked with converting derivative fields (p;q) back to imagespace I; typically, a Poisson equation solver is used for this task. But what if we have altered (p;q) so that this pair (p;q) is no longer integrable? Then we have to project onto integrable gradient data that will indeed reintegrate to an approximation of the original image. For example, we may wish to alter (p;q) so as to emphasize or de-emphasize some aspects of the image, e.g. ameliorating wrinkles in skin images (or indeed enhancing them in the case of ageing a face image). Here, we propose a new gradient kernel that retains part of the original image, regularising the reintegration back into the image domain. We compare our approach with the Screened-Poisson method which includes a term λ times a “screen” term that moves the solution image back closer to the input image. Effectively, we are doing a similar adjustment, but we show that the results are a good deal better than using Screened-Poisson, which tends to overly blur the output. Moreover, in Screened-Poisson one must choose a value for λ, which may be different for every image – here we determine that our new kernel method does not need to adapt to each image yet delivers comparable or better results.
We propose a new anisotropic diffusion process for removing noise from MRI images without distorting the edges. The method is based on a simple principle: any diffusion that increases a gradient at neighbouring pixels should be prohibited. From this principle, we deduce an inequality that allows diffusion along the edges but not across them. We introduce promising results using synthetic data with various types of noise as well as real MRI scans.
An experiment was carried out to investigate separation and CMF effects on colour-difference evaluation using display colours. In total, 1120 sample pairs around 5 CIE recommended colour centres were assessed 20 times using the grey-scale method. Sample pairs were selected to have colour-difference of 4 and 8 CIELAB units, include or exclude separation between two colours on a pair, have four fields of view (FoVs), 2º, 4º, 10º and 20º. The experiment results were used to test 3 colour-difference equations or uniform colour spaces, CIELAB, CIEDE2000 and CAM16-UCS. For separation (S) sample pairs, CIEDE2000 performed the best, followed by CAM16-UCS and CIELAB the worst. For no-separation (NS) sample pairs, all models gave worse performance than separation (S) sample pairs. The parametric formula derived earlier was verified to predict colour-difference for sample pairs to have no-separation line. Five colour matching functions (CMFs), CIE 1931, CIE 1964, CIE 2006-2º, CIE 2006-10º and 2006-4º were tested and the results indicated very small CMF effect on calculating colour-difference.
The exploration of the Solar System using unmanned probes and rovers has improved the understanding of our planetary neighbors. Despite a large variety of instruments, optical and near-infrared cameras remain critical for these missions to understand the planet’s surrounding, its geology but also to communicate easily with the general public. However, missions on planetary bodies must satisfy strong constraints in terms of robustness, data size, and amount of onboard computing power. Although this trend is evolving, commercial image-processing software cannot be integrated. Still, as the optical and a spectral information of the planetary surfaces is a key science objective, spectral filter arrays (SFAs) provide an elegant, compact, and cost-efficient solution for rovers. In this contribution, we provide ways to process multi-spectral images on the ground to obtain the best image quality, while remaining as generic as possible. This study is performed on a prototype SFA. Demosaicing algorithms and ways to correct the spectral and color information on these images are also detailed. An application of these methods on a custom-built SFA is shown, demonstrating that this technology represents a promising solution for rovers.
In recent years, image processing has proven to be a great tool to help document, preserve, and restore art pieces, especially visual art. One example is using colorization techniques when the original color information of the image has been lost or is unavailable. To expand on that, we used Ghent Altarpiece as the study case. One of the panels of this polyptych has been lost, and only an archival photograph exists. Using the state-of-the-art colorization method, we want to digitally restore what has been lost in its original form. In this work, we proposed a pipeline that consists of a colorization transformer (ColTran) trained on the captured patch-based imaging data of the Ghent Altarpiece. We evaluated the proposed pipeline and addressed its strengths and weaknesses. Moreover, we presented planned future steps and improvements for this project.
For a few years, the automotive industry has produced new cars with continuous changing display models, this by combining display sizes, forms, and technologies all together to bring new experiences to the users. In this paper we will present the color matching solution implemented for a new car display system where regular LCD display technology and LED lighting tiles are mixed together. The solution we proposed is based on accepted display model providing the transformation device RGB space to CIE XYZ independent color space and in reverse. The approach we followed choose the LCD display as reference, then transformation matrix is derived to modify the RGB LED control values. Once the color matching operation is applied, the color difference between the two display areas is greatly reduced.
The colors of pigments and dyes are affected by light exposure. Light-induced color change has an impact on various industrial and artistic applications where colored materials are frequently exposed to light throughout their life-cycle. For this reason, it is beneficial to understand the fading behaviour of pigments and simulate future degradation. In this article, we are proposing a method to forecast color change of pigments based on time series analysis. To begin with, we collect fading data from real objects with a microfadeometer, which records the color coordinates after every second of light exposure. Then, we treat this data as a time series, test for its stationarity and fit it with autoregressive integrated moving average (ARIMA) models. Finally, using a train-test split, we validate the accuracy of the ARIMA models in predicting color degradation of pigments and dyes.
In order to reproduce the colour appearance between real scene and images on self-luminous display, this study conducted a series of psychophysical experiments using threshold method. Three types of real scenes were built up in a lighting room, including painting, fruit and vegetable, skin colour chart. Sixteen adapting conditions were designed including four CCTs (3000K, 4500K, 6500K, 8000K) and 4 luminance levels (10lux, 100lux, 500lux, 1000lux). Four displays with different sizes were studied. The result indicated that the colour appearance of real scene and the image on the display were different, especially for low CCT and luminance level. The contents of scene and size of display didn’t show a significate impact. The prediction performance of CIECAM16 was tested, and a revised formulation was proposed with high accuracy.
A psychophysical study on two series of printed metallized surfaces, which both consisted of ten samples was performed. Two groups participated in the experiment. These were experts, who regularly judge the appearance of printed samples at their daily work, and amateurs who do not regularly visually judge and compare samples. For the experiments, a special light booth for conducting visual experiments with focus on gloss was designed and a ranking experiment was worked out. It was investigated how observers look at these kind of surfaces when asked to judge their glossiness, lightness, roughness, the sharpness of reflected images, and metallicity. All samples had the same size and nearly no hue but differed in gloss and texture. It was examined how the ratings of the targeted characteristics of appearance correlate with each other, and how they correlate with the gloss measured at the specular angles of 20°, 60°, and 85° and the distinctness-of-image measured with an IQ-S gloss meter. Additionally, observers were inquired for their individual understanding of gloss.
Research on human lightness perception has revealed important principles of how we perceive achromatic surface color, but has resulted in few image-computable models. Here we examine the performance of a recent artificial neural network architecture in a lightness matching task. We find similarities between the network’s behaviour and human perception. The network has human-like levels of partial lightness constancy, and its log reflectance matches are an approximately linear function of log illuminance, as is the case with human observers. We also find that previous computational models of lightness perception have much weaker lightness constancy than is typical of human observers. We briefly discuss some challenges and possible future directions for using artificial neural networks as a starting point for models of human lightness perception.
We consider the problem of estimating surface-spectral reflectance with a smoothness constraint from image data. The total variation of a spectral reflectance over the visible wavelength range is defined as the measure of smoothness. A penalty on roughness, equivalent to smoothness, is added to the performance index to estimate the spectral reflectance functions. The optimal estimates of the spectral reflectance functions are determined to minimize a total cost function consisting of the estimation error and the roughness of the spectral functions. An RGB camera and multiple LED light sources are used to construct the multispectral image acquisition system. We model the observed images using spectral sensitivities, illuminant spectrum, unknown spectral reflectance, a gain parameter, and an additive noise term. The estimation algorithms are developed for the two estimation methods of PCA and LMMSE. The optimal estimators are derived based on the least-square criterion for PCA and the mean squared error minimization criterion for LMMSE. The feasibility of the proposed method is shown in an experiment using three mobile phone cameras. It is confirmed that the optimal estimators improve the accuracy for both original PCA and LMMS estimators.
Some natural scenes show a reduced set of colors. These scenes are often encountered in space imaging, for instance for ocean observation which deals with hues of blue and for Mars exploration which deals with hues of “yellowish-brown”. In this context the interest of performing hue-specific (or scene-specific) color corrections for the reconstruction of these images is tested. The study is performed on the Next Generation Target (Avian Rochester, LLC - 130 color patches) for both the color correction matrix computation and efficiency testing. The results show that such hue-specific corrections are efficient on the hues of interest, and evaluate the impact on subsidiary hues.
