Color Representation Research Articles

AgriGAN: unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype

Artificially extracted agricultural phenotype information exhibits high subjectivity and low accuracy, while the utilization of image extraction information is susceptible to interference from haze. Furthermore, the effectiveness of the agricultural image dehazing method used for extracting such information is limited due to unclear texture details and color representation in the images. To address these limitations, we propose AgriGAN (unpaired image dehazing via a cycle-consistent generative adversarial network) for enhancing the dehazing performance in agricultural plant phenotyping. The algorithm incorporates an atmospheric scattering model to improve the discriminator model and employs a whole-detail consistent discrimination approach to enhance discriminator efficiency, thereby accelerating convergence towards Nash equilibrium state within the adversarial network. Finally, by training with network adversarial loss + cycle consistent loss, clear images are obtained after dehazing process. Experimental evaluations and comparative analysis were conducted to assess this algorithm's performance, demonstrating improved accuracy in dehazing agricultural images while preserving detailed texture information and mitigating color deviation issues.

Color representations of normals and congenital red–green color deficiencies based on differential scaling of color‐names and color‐naming experiments

Color representations of normals and congenital red–green color deficiencies based on differential scaling of color‐names and color‐naming experiments

Dual-stage color calibration of UAV imagery using multivariate regression and deep learning

Accurate color representation in UAV-based imagery is critical in high-throughput plant phenotyping and precision agriculture for monitoring crop health and identifying nutrient deficiencies or water stress. However, achieving color accuracy is challenging due to varying lighting conditions and flight altitudes in UAV operations. To overcome these challenges, we propose an automated dual-phase approach for consistent color calibration in UAV imagery, integrating traditional color calibration methods with cutting-edge deep learning algorithms. In the first phase, we derived a color calibration matrix (M) for four color boards placed within the imaging area. This involved mapping the observed color values in the UAV images to their true color values, as measured by a colorimeter, using multivariate linear regression (MLR). The individual matrices from each board were then merged into a single, weighted average matrix, which was then applied to the UAV images for color correction, resulting in standardized color outputs. In the second phase, we developed a deep learning model, trained on the MLR-corrected images as a ground truth. This model assigns varying weights for different pixels across the image scene, translating the image from a distorted to a color-corrected image using image-to-image regression without manual intervention. We validated this approach using 184 UAV images of various sizes, ranging from 3969 × 2589 to 15257 × 15098 pixels, captured under a wide array of lighting conditions and altitudes. The results show that our deep learning model significantly outperforms existing methods, with a marginal color error of 1.52 %. This two-phase color correction method greatly improves operational efficiency by removing the necessity for physical color boards in each image and allowing for automated processing after training.

Widening the Gamut of Structural Colors of Gold via Insulator–Metal Bilayer Coatings

Tuning the color of Au has been a longstanding problem in the luxury industry. Conventional approaches, involving Au alloying, compromise purity and demand distinct alloy compositions for each hue. This study demonstrates a lithography‐free method for generating structural colors on a gold surface by adjusting the thickness of titanium dioxide, a high‐index dielectric. While color tuneability is limited if TiO2 is coated directly on the Au surface, a range of vivid colors can be generated if a 50−100 nm thick AuAl2 underlayer is used. AuAl2, an accepted alloy for purple gold, broadens the color gamut, providing a protective coating without diminishing gold purity. The reflectance dip of the bilayer structure exhibits a significant red shift with increasing thickness of the TiO2 layer, allowing diverse colors by TiO2 insulator tuning. Simulation studies corroborate experimental results, affirming that coating a TiO2 layer on the AuAl2 underlayer yields a wide range of colors. This method, based on thin‐film interference, shows promise for widespread use, offering a broad spectrum of structural colors in an industry striving for diverse Au color representation.

Potassium Iodide Doping for Vacancy Substitution and Dangling Bond Repair in InP Core-Shell Quantum Dots.

This work highlights the novel approach of incorporating potassium iodide (KI) doping during the synthesis of In0.53P0.47 core quantum dots (QDs) to significantly reduce the concentration of vacancies (i.e., In vacancies; VIn-) within the bulk of the core QD and inhibit the formation of InPOx at the core QD-Zn0.6Se0.4 shell interfaces. The photoluminescence quantum yield (PLQY) of ~97% and full width at half maximum (FWHM) of ~40 nm were achieved for In0.53P0.47/Zn0.6Se0.4/Zn0.6Se0.1S0.3/Zn0.5S0.5 core/multi-shell QDs emitting red light, which is essential for a quantum-dot organic light-emitting diode (QD-OLED) without red, green, and blue crosstalk. KI doping eliminated VIn- in the core QD bulk by forming K+-VIn- substitutes and effectively inhibited the formation of InPO4(H2O)2 at the core QD-Zn0.6Se0.4 shell interface through the passivation of phosphorus (P)-dangling bonds by P-I bonds. The elimination of vacancies in the core QD bulk was evidenced by the decreased relative intensity of non-radiative unpaired electrons, measured by electron spin resonance (ESR). Additionally, the inhibition of InPO4(H2O)2 formation at the core QD and shell interface was confirmed by the absence of the {210} X-ray diffraction (XRD) peak intensity for the core/multi-shell QDs. By finely tuning the doping concentration, the optimal level was achieved, ensuring maximum K-VIn- substitution, minimal K+ and I- interstitials, and maximum P-dangling bond passivation. This resulted in the smallest core QD diameter distribution and maximized optical properties. Consequently, the maximum PLQY (~97%) and minimum FWHM (~40 nm) were observed at 3% KI doping. Furthermore, the color gamut of a QD-OLED display using R-, G-, and B-QD functional color filters (i.e., ~131.1%@NTSC and ~98.2@Rec.2020) provided a nearly perfect color representation, where red-light-emitting KI-doped QDs were applied.

MCRNet: Underwater image enhancement using multi-color space residual network

The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images, which can impede the ever-growing demand for underwater robot operations. To address these issues, we propose a Multi-Color space Residual Network (MCRNet) for underwater image enhancement. Our method takes advantage of the unique features of color representation in the RGB, HSV, and Lab color spaces. By utilizing the distinct feature representations of images in different color spaces, we can highlight and fuse the most informative features of the three color spaces. Our approach employs a self-attention mechanism in the multi-color space feature fusion module. Extensive experiments demonstrate that our method achieves satisfactory results in color correction and contrast improvement of underwater images, particularly in severely degraded scenes. Consequently, our method outperforms state-of-the-art methods in both subjective visual comparison and objective evaluation metrics.

Visual perception and user satisfaction in video see-through head-mounted displays: a mixed-methods evaluation

ObjectiveOur study addresses the challenges limiting the adoption of Extended Reality (XR) Head-Mounted Displays (HMDs), mainly focusing on device quality and cybersickness. We aim to investigate the impact of hardware and software on user experience and task performance while wearing Video See-Through (VST) HMDs. We employ a novel methodology designed to bridge the gaps identified in previous research.MethodsThis study uses a convergent mixed-methods approach, combining qualitative and quantitative data in a within-subjects evaluation involving 20 participants. This comprehensive evaluation examines visual perception, visual quality, and user experience through a range of tasks. Usability, comfort, and cybersickness are assessed, with insights derived from both user performance metrics and subjective measures collected through in-depth interviews and comments. The study includes three distinct HMDs—two prototypes (PD1 and PD2) and one commercial device (CD1)—to provide a broad analysis of the technology.FindingsOur findings reveal that while participants were generally satisfied with VST mode, their preferences varied across devices. CD1 was preferred for its realistic color representation and superior reading task performance due to its high-resolution display and camera. However, visual disturbances and temporal issues differed across devices, with CD1 exhibiting fewer artifacts when stationary but showing more disturbances when participants were moving. Participants found PD1 and PD2 more comfortable for extended use and fewer cybersickness symptoms, but they highlighted color and display resolution issues. These variations underscore the importance of considering both qualitative and quantitative measures in HMD evaluations.ConclusionThis mixed-methods evaluation emphasizes the limitations of relying solely on visual perception performance measures for VST HMDs. By integrating both quantitative and qualitative insights, we offer a more detailed evaluation framework to identify design flaws and user experience issues that quantitative metrics alone might miss. This methodology contributes to the field by illustrating how a mixed-methods approach provides a broader perspective on XR technology, guiding future improvements and enhancing VST adoption in future applications.

Ultrahigh-fidelity full-color holographic display via color-aware optimization

Holographic display offers the capability to generate high-quality images with a wide color gamut since it is laser-driven. However, many existing holographic display techniques fail to fully exploit this potential, primarily due to the system’s imperfections. Such flaws often result in inaccurate color representation, and there is a lack of an efficient way to address this color accuracy issue. In this study, we develop a color-aware hologram optimization approach for color-accurate holographic displays. Our approach integrates both laser and camera into the hologram optimization loop, enabling dynamic optimization of the laser’s output color and the acquisition of physically captured feedback. Moreover, we improve the efficiency of the color-aware optimization process for holographic video displays. We introduce a cascade optimization strategy, which leverages the redundant neighbor hologram information to accelerate the iterative process. We evaluate our method through both simulation and optical experiments, demonstrating the superiority in terms of image quality, color accuracy, and hologram optimization speed compared to previous algorithms. Our approach verifies a promising way to realize a high-fidelity image in the holographic display, which provides a new direction toward the practical holographic display.

Non-invasive spectroscopic analyses of M. E. Chevreul’s colour plates: shedding new light on 19th century colour theory representation

During the early 19th century, colour’s theories flourished. In this context, Michel-Eugène Chevreul (1786–1889) developed a three-dimensional hemispherical colour space, reproduced and used for several decades by manufacturers, scientists, artists, architects, gardeners and designers, among others, to easily identify and classify colours. Despite the major influence and variety of chromatic spaces created by M. E. Chevreul, they have been the subject of a few scientific studies until now. This study will provide new information on the evolution of the printing technique, the materiality and the visual appearance of six different colour circles printed in 1847, 1855, 1861 and 1864. A dedicated analytical methodology was implemented combining visible and near-infrared reflectance imaging, X-ray fluorescence, Raman, short-wave infrared fibre optic reflectance and mid-infrared Fourier transform spectroscopies. This material identification revealed the coexistence of newly synthetized inorganic pigments, such as lead chromate, Prussian blue or Emerald/Scheele green, with established organic and inorganic pigments, such as cochineal lake, vermilion or minium. This study revealed a temporal evolution of the printing technique that leads to an improved colour representation.

Participatory and User-Centered Design for Hazard Mapping: A Case Study from Achoma, Peru

Geologic hazards affect the community of Achoma, in Peru’s Colca Canyon. Building on a rockfall hazard characterization study, this article engages the principles of participatory mapping and user-centered design for the tailoring of hazard maps to the context. Two workshops were conducted with community members to gauge their perceptions of sample maps, preferences for design elements, and potential uses of maps, and to refine accuracy of map features. Each workshop consisted of three activities in which participants marked up and discussed sample maps. The activities focused on (1) basemap preferences and locally relevant features, (2) intelligibility of rockfall hazard information, and (3) intelligibility of color schemes and representations of hazard severity ratings. The outcomes of the workshops were used to inform design updates for a final hazard map, which was provided to community members and local officials. The study identified many design preferences that were similar to those found in prior studies and expanded the scope of participatory and user-centered design research in hazard mapping, which has mostly been conducted in the Global North.

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky.

Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of "predatory"-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.

SAMCF: Adaptive global style alignment and multi-color spaces fusion for joint optic cup and disc segmentation

The optic cup (OC) and optic disc (OD) are two critical structures in retinal fundus images, and their relative positions and sizes are essential for effectively diagnosing eye diseases. With the success of deep learning in computer vision, deep learning-based segmentation models have been widely used for joint optic cup and disc segmentation. However, there are three prominent issues that impact the segmentation performance. First, significant differences among datasets collecting from various institutions, protocols, and devices lead to performance degradation of models. Second, we find that images with only RGB information struggle to counteract the interference caused by brightness variations, affecting color representation capability. Finally, existing methods typically ignored the edge perception, facing the challenges in obtaining clear and smooth edge segmentation results. To address these drawbacks, we propose a novel framework based on Style Alignment and Multi-Color Fusion (SAMCF) for joint OC and OD segmentation. Initially, we introduce a domain generalization method to generate uniformly styled images without damaged image content for mitigating domain shift issues. Next, based on multiple color spaces, we propose a feature extraction and fusion network aiming to handle brightness variation interference and improve color representation capability. Lastly, an edge aware loss is designed to generate fine edge segmentation results. Our experiments conducted on three public datasets, DGS, RIM, and REFUGE, demonstrate that our proposed SAMCF achieves superior performance to existing state-of-the-art methods. Moreover, SAMCF exhibits remarkable generalization ability across multiple retinal fundus image datasets, showcasing its outstanding generality.

The resurgence of the G(2) group for the strong sector and the emergence of dark matter

G(2) is the smallest exceptional group and it is the simplest and viable gauge group to minimally extend the strong interaction sector: G(2) includes the group SU(3) of Quantum Chromodynamics (QCD) as a maximal subgroup and it is equipped with six additional gluons that can acquire mass via a Higgs mechanism driven by a new Higgs particle and constitute dark matter. In this article I want to describe how the exceptional G(2) group can be a physical gauge group, capable of extending the Standard Model (SM) of particles and including a versatile dark sector, which is compatible with experimental observations. In fact, due to its peculiar mathematical features, the group G(2) manifests some complex features, not properly considered in literature, which guarantee its correct use in physics, as its {3}⊕{3‾} decompositions w.r.t. SU(3) can acquire a complex structure. The resulting framework can be a solid Beyond Standard Model (BSM) solution for the dark matter (DM) problem, in the form of massive complex scalar glueballs, and it includes the proper color representations for quarks and leptons. Several quantum field theory features are discussed, like the G(2) coupling constant running and its spectrum before and after the phase transition, along with all the DM astrophysical realizations, in order to present the unexpected potential of this gauge group.

Color symmetry and confinement as an underlying superconformal structure in holographic QCD

Dedicated to the memory of our colleague, Harald Fritzsch, who, together with Murray Gell-Mann, introduced the color quantum number as the exact symmetry responsible for the strong interaction, thus establishing quantum chromodynamics (QCD) as a fundamental non-Abelian gauge theory. A basic understanding of hadron properties, however, such as confinement and the emergence of a mass scale, from first principles QCD has remained elusive: Hadronic characteristics are not explicit properties of the QCD Lagrangian and perturbative QCD, so successful in the large transverse momentum domain, is not applicable at large distances. In this article, we shall examine how this daunting obstacle is overcome in holographic QCD with the introduction of a superconformal symmetry in anti de Sitter (AdS) space which is responsible for confinement and the introduction of a mass scale within the superconformal group. When mapped to light-front coordinates in physical spacetime, this approach incorporates supersymmetric relations between the Regge trajectories of meson, baryon and tetraquark states which can be visualized in terms of specific [Formula: see text] color representations of quarks. We will also briefly discuss here the implications of holographic models for QCD color transparency in view of the present experimental interest.

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

Color discrimination repetition distorts color representations

Perceptual learning is the improvement of perceptual performance after repeated practice on a perceptual task. Studies on perceptual learning in color vision are limited. In this study, we measured the impact of color discrimination repetitions at a specific base color on color perception for entire hues. Participants performed five sessions of color discrimination training (200 or 300 trials per session) over five days, at colors on either the negative or positive direction of the L-M color axis, based on group assignment. We administered three color perception assessments (unique hues, color category boundaries, and color appearance) before and after the sessions to evaluate perceptual changes after training. The results showed declines in color discrimination thresholds after training, as expected. Additionally, the training influenced outcomes across all three assessment types. After the training, the perceived color appearance changed near the trained color along the stimulus hue, and some of the unique hues and the color category boundaries moved significantly toward the trained color. These findings indicate that short-term repetitions of color discrimination training can alter color representations in the visual system, distorting color perception around the trained color.

Microanalytical investigation of brazilian coat of arms paintings on paper

Heraldry in Latin American art of the twentieth century can be considered rare. In this context, the material composition of coat of arms paintings on paper attributed to the Brazilian painter José Wasth Rodrigues is investigated for the first time using a microanalytical approach. Microsamples from 5 municipal coats of arms dated 1932 were analysed using Raman microscopy, Attenuated Total Reflection Fourier Transform Infrared spectroscopy (ATR-FTIR), and Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM-EDS). SEM-EDS results revealed the artist's choice of using metallic aluminium and brass in the representation of silver and gold colours, respectively. Raman microscopy analyses showed the use of traditional inorganic pigments, such as ultramarine blue, indigo, Prussian blue, yellow chromates and vermillion. In contrast, the results showed that the painting with an unusual signature attributed to the artist (W. instead of J.W.R, commonly found) contains synthetic organic dyes Orange 5, Red 53, and Yellow 1 (introduced in the 1900s). This discovery raised hypothesis of questioning authorship for this particular painting. ATR-FTIR analysis of a paint fragment from one painting confirmed the use of gum arabic as a binder. ATR-FTIR analyses of efflorescences collected from a blue paint layer of a particular coat of arms suggest protein and lipid content, raising suspicion of either the use of the tempera technique in this particular paint or remains of a past microorganism activity. Robust information was obtained on the chemical characterization of the coat of arms, enhancing comprehension of the painting techniques employed by the artist and providing insight into the production of heraldic art in Brazil at the beginning of the 20th-century.

In vitro Comparison of the Mechanical and Optical Characteristics of 5 Disposable Flexible Ureteroscopes

Introduction: Disposable (single-use) flexible ureteroscopes are alternatives to reusable ureteroscopes. With their superior surgical efficacy and safety in the presence of upper urinary calculi, disposable ureteroscopes aim to overcome the main limitations of conventional reusable ureteroscopes. However, studies on the performance of the most recently developed models of single-use flexible ureteroscopes are scarce. This study aimed to compare the in vitro performance of several recently introduced, single-use, flexible ureteroscopes. Methods: Five disposable flexible ureteroscopes were tested in vitro to evaluate their mechanical and optical characteristics. To this end, their degrees of deflection, irrigation flow rates, and image qualities were investigated. The models examined were Innovex US31-B12, OTU-100RR, Redpine RP-U-C12, Sciavita SUV-2A-B, and Seplou URS3016E. Their performance was also compared with that of a reusable flexible ureteroscope, Olympus URV-F. Results: The OTU device had the highest degrees of deflection and the smallest loop diameter of the disposable ureteroscopes. The single-use ureteroscopes had identical image resolutions at a distance of 1 cm. The Innovex and Redpine devices had the best color representation. Conclusions: Of the tested disposable ureteroscopes, the OTU device had the best mechanical attributes, given its small loop diameter, high deflection angles, and low irrigation flow loss. As to their optical properties, the resolutions of all 5 single-use models were identical at an image distance of 1 cm.

Cross-domain colorization of unpaired infrared images through contrastive learning guided by color feature selection attention

In challenging lighting conditions, infrared detectors have become vital tools for enhancing visual perception, overcoming the limitations of visible cameras. However, inherent imaging principles and manufacturing constraints confine infrared imaging systems to grayscale, significantly impacting their utility. In comparison to visible imagery, infrared images lack detailed semantic information, color representation, and suffer from reduced contrast. While existing infrared image colorization techniques have made significant progress in improving color quality, challenges such as erroneous semantic color prediction and blurred depiction of fine details persist. Acquiring paired color images corresponding to real-world infrared scenarios poses substantial difficulties, exacerbating challenges in cross-domain colorization of infrared images. To address these critical issues, this paper introduces an innovative approach utilizing contrastive learning for unsupervised cross-domain mapping between unpaired infrared and visible color images. Additionally, we introduce a color feature selection attention module guiding rational infrared image coloring. The proposed method employs the Residual Fusion Attention Network (RFANet) as a generator, enhancing the encoder’s ability to represent color and structural features. Furthermore, to ensure structural content consistency and enhance overall color style matching accuracy, we design a comprehensive joint global loss function integrating both detailed content and color style. Experimental evaluations on publicly available datasets demonstrate the superior performance of the proposed unsupervised cross-domain colorization method for infrared images compared to previous approaches.

Brown Skin Girl: Mapping Negotiations of Colorism, Digital Resistance, and Black Girlhood

This study delves into the intricate relationship between the symbolic representation of skin color in virtual avatars and its profound impact on people's self-value within American society. Over time, white skin has been bestowed with societal significance, often associated with power and privilege. This research addresses the profound implications of these avatar choices on individuals' self-identity and the reinforcement of prevailing cultural norms. It highlights the subtle yet impactful ways in which modern technology intertwines with and reinforces societal attitudes toward race and appearance. By shedding light on this phenomenon, this paper aims to encourage further discussions on the potential consequences of associating skin color with value and self-worth in virtual spaces.