Color Calibration Research Articles

TANAGER: Design and Validation of an Automated Spectrogoniometer for Bidirectional Reflectance Studies of Natural Rock Surfaces

AbstractLaboratory measurements of reflectance spectra of rocks and minerals at multiple viewing geometries are important for interpreting spacecraft data of planetary surfaces. However, efficiently acquiring such measurements is challenging, as it requires a custom goniometer that can accommodate multiple, bulky samples beneath a movable light source and detector. Most spectrogoniometric laboratory work to date has focused on mineral mixtures and particulates, yet it is also critical to characterize natural rock surfaces to understand the influence of texture and alteration. We designed the Three‐Axis N‐sample Automated Goniometer for Evaluating Reflectance (TANAGER) specifically to rapidly acquire spectra of natural rock surfaces across the full scattering hemisphere. TANAGER has its light source and the spectrometer's fiber optic mounted on rotating and tilting arcs, with a rotating azimuth stage and six‐position sample tray, all of which are fully motorized and integrated with a Malvern PanAnalytical ASD FieldSpec4 Hi‐Res reflectance spectrometer. Using well‐characterized color calibration targets, we have validated the accuracy and repeatability of TANAGER spectra. We also confirm that the system introduces no discernible noise or artifacts. All design schematics and control software for TANAGER are open‐source and available for use and modification by the larger scientific community.

Estimating SPAD, Nitrogen Concentration, and Chlorophyll Content in Rice Leaves using Calibrated Smartphone Digital Image

Laboratory analysis is commonly used to determine nitrogen and chlorophyll content. However, smartphones can serve as rapid, mobile, and non-destructive tools for this purpose. An equation can be created to calculate nitrogen and chlorophyll content by analyzing color parameters from digital images of rice leaves. An examination was performed on 86 rice leaf samples from the maximum tillering and mature stages. Rice leaf photos were taken with a smartphone in natural outdoor lighting. Color calibration with Spydercheckr was needed to adjust for lighting conditions. Uncalibrated and calibrated image data were analyzed to determine RGB values converted into CIELAB color space. The L*, a*, and b* values had a significant correlation with SPAD parameters, nitrogen concentration, chlorophyll a, b, and total chlorophyll content. This connection was higher after image calibration. The study found that smartphone images could predict SPAD values with 87.9% to 92.3% precision, depending on color space. Using a smartphone digital picture of L* and a* values, N content could be estimated with 84.7% and 81.9% accuracy. Average accuracy for chlorophyll a, b, and total chlorophyll content was 65% to 76%. This study shows smartphone images can estimate rice leaf SPAD and nitrogen content.

Analysis of the Application of Digital Color Calibration Technology in Film Post-Production: Take the Short Film "Return to Oasis" as a Case Study

Analysis of the Application of Digital Color Calibration Technology in Film Post-Production: Take the Short Film "Return to Oasis" as a Case Study

Exposure difference network for low-light image enhancement

Low-light image enhancement aims to simultaneously improve the brightness and contrast of low-light images and recover the details of the visual content. This is a challenging task that makes typical data-driven methods suffer, especially when faced with severe information loss in extreme low-light conditions. In this work, we approach this task by proposing a novel exposure difference network. The proposed network generates a set of possible exposure corrections derived from the differences between synthesized images under different exposure levels, which are fused and adaptively combined with the raw input for light compensation. By modeling the intermediate exposure differences, our model effectively eliminates the redundancy existing in the synthesized data and offers the flexibility to handle image quality degradation resulting from varying levels of inadequate illumination. To further enhance the naturalness of the output image, we propose a global-aware color calibration module to derive low-frequency global information from inputs, which is further converted into a projection matrix to calibrate the RGB output. Extensive experiments show that our method can achieve competitive light enhancement performance both quantitatively and qualitatively.

FPGA-based control system for real-time driving of UHD Micro-LED display with color calibration

Micro-LED technology offers numerous advantages, including high brightness, low power consumption, and superior color performance. However, driving micro LED displays requires complex control algorithms and high-speed data processing. To address these challenges, this paper presents the development of a real-time FPGA-based control system for a 68-inch 4K/60Hz micro-LED display. The objective of this project was to create a high-performance control system capable of driving the micro-LED display with precise color accuracy, supporting 10-bit color depth for enhanced color rendition. One crucial aspect of the system is color calibration, which ensures accurate color reproduction across the display, maintaining consistent and vibrant colors. By incorporating advanced color calibration techniques, the system achieves excellent color consistency and fidelity, providing a visually stunning viewing experience. Moreover, the system incorporates hot plug functionality, allowing for seamless reconnection of the display after Ethernet disconnection. This feature ensures uninterrupted operation and enhances user experience. In FPGA design, the proposed system demonstrates the feasibility and effectiveness of real-time control in driving micro-LED displays, offering improved color performance and reliable operation in various applications.

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.

High-fidelity color characterization in virtual reality across head mounted displays, game engines, and materials.

We present a comprehensive colorimetric analysis of three head mounted displays (HMDs) - HTC Vive Pro Eye, Pimax 8K X DMAS, and Varjo Aero - focusing on their color calibration and uniformity across different game engines (Unity and Unreal) and for different materials/shaders. We developed a robust methodology combining hardware and software tools, including spectroradiometry and imaging colorimetry, to characterize and calibrate these HMDs for accurate color reproduction. The study showcases substantial advancements in colorimetric accuracy, with a reduction in the average deltaE00 of 90% or more across all tested HMDs and conditions. This level of color reproduction quality is below human discrimination thresholds, ensuring that any color inaccuracies remain imperceptible to the human eye. We also identified key areas for improvement, particularly in display uniformity, which could impact peripheral color reproduction. By making our tools and code publicly available, this study aims to facilitate future research and development in virtual reality (VR) technology, emphasizing the importance of color fidelity in virtual environments. The new insight enabled by our work is the extension and application of a traditional calibration method to currently available HMDs.

58‐1: A Novel On‐line, Fast Color Correction by Machine Learnings

A new efficient color correction method, utilizing neural network models, is proposed by this study for OLED panels in production, offering a transformative approach to achieve accurate and rapid color calibration. Utilizing transfer learning with a limited dataset, it creates color conversion matrices for AI‐driven color correction. The process involves selecting a similar baseline model from prior batches with a ΔE00 below 1, then refining it using a representative dataset. This model corrects white point and overall color, achieving a ΔE00 less than 2. It corrects 300 data points in less than 4 seconds, demonstrating both speed and accuracy.

SPNet: Semantic preserving network with semantic constraint and non-semantic calibration for color constancy

Recent methods introduce semantics obtained from pre-trained classification models into color constancy to guide the model in learning object-color mapping, thereby improving the illumination estimation ability. However, the task discrepancy between classification and color constancy leads to a representation learning gap, resulting in semantic loss during training. Moreover, semantic-based methods emphasize semantic regions, leading to the neglect of clues contained in non-semantic regions. Overall, semantic-based methods face the challenge of underutilizing critical information. To address this problem, we propose a Semantic Preserving Network (SPNet). We first design a Semantic Constraint Module (SCM) in SPNet. SCM provides semantic constraints to ensure uninterrupted semantic knowledge transfer during training, which prevents semantic loss. Additionally, we further propose an Auxiliary Calibration Module (ACM). ACM explores background regions with a restricted range of innate colors. The high consistency property of their color contributes to the calibration of illumination colors. Meanwhile, ACM recalibrates local–global consistency to avoid large estimation bias in other background regions where illumination clues are insufficient. Extensive experiments on the benchmark datasets show that our method achieves superior performance without extra inference consumption.

Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique

Information technology and statistical modeling have made significant contributions to smart agriculture. Machine vision and hyperspectral technologies, with their non-destructive and real-time capabilities, have been extensively utilized in the non-destructive diagnosis and quality monitoring of crops and seeds, becoming essential tools in traditional agriculture. This work applies these techniques to address the color classification of rapeseed, which is of great significance in the field of rapeseed growth diagnosis research. To bridge the gap between machine vision and hyperspectral technology, a framework is developed that includes seed color calibration, spectral feature extraction and fusion, and the recognition modeling of three seed colors using four machine learning methods. Three categories of rapeseed coat colors are calibrated based on visual perception and vector-square distance methods. A fast-weighted visibility graph method is employed to map the spectral reflectance sequences to complex networks, and five global network attributes are extracted to fuse the full-band reflectance as model input. The experimental results demonstrate that the classification recognition rate of the fused feature reaches 0.943 under the XGBoost model, confirming the effectiveness of the network features as a complement to the spectral reflectance. The high recognition accuracy and simple operation process of the framework support the further application of hyperspectral technology to analyze the quality of rapeseed.

Colorimetric sensor array for versatile detection and discrimination of model analytes with environmental relevance

In the current work, a rapid, simple, low-cost, and sensitive smartphone-based colorimetric sensor array coupled with pattern-recognition methods was proposed for the determination and differentiation of some organic and inorganic bases (i.e., OH−, CO32−, PO43−, NH3, ClO−, diethanolamine, triethanolamine) as model compounds. The sensing system has been designed based on color-sensitive dyes (Fuchsine, Giemsa, Thionine, and CoCl2) which were used as sensor elements. The color changes of a sensor array were observed by the naked eye. The color patterns were recorded using digital imaging in a three-dimensional (red, green, and blue) space and quantitatively analyzed with color calibration techniques. Distinctive colorimetric patterns for target bases via linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) were observed. The results indicated that the analytes related to each class (at the different concentration levels in the range of 0.001–1.0 mol L−1) were clustered together in the canonical discriminant plot and HCA dendrogram with high sensitivity and an overall precision of 85%. Furthermore, the first function factor of LDA correlated with the concentration of each target analyte in a correlation coefficient (R2) range of 0.864–0.996. These described procedures based on the colorimetric sensor array technique could be a promising candidate for practical applications in package technology and facile detection of pollutants.

Self-Adaptive Colour Calibration of Deep Underwater Images Using FNN and SfM-MVS-Generated Depth Maps

The task of colour restoration on datasets acquired in deep waters with simple equipment such as a camera with strobes is not an easy task. This is due to the lack of a lot of information, such as the water environmental conditions, the geometric setup of the strobes and the camera, and in general, the lack of precisely calibrated setups. It is for these reasons that this study proposes a self-adaptive colour calibration method for underwater (UW) images captured in deep waters with a simple camera and strobe setup. The proposed methodology utilises the scene’s 3D geometry in the form of Structure from Motion and MultiView Stereo (SfM-MVS)-generated depth maps, the well-lit areas of certain images, and a Feedforward Neural Network (FNN) to predict and restore the actual colours of the scene in a UW image dataset.

36‐3: Research on Partitioned Color Calibration Principle and Frame Rate Reduction Technology Based on Liquid Crystal Displays

36‐3: Research on Partitioned Color Calibration Principle and Frame Rate Reduction Technology Based on Liquid Crystal Displays

Developing an Automated Spectrophotometer with RGB LED and Light Sensor Using Arduino Microcontroller

This research used RGB LED (Red Green Blue Light Emission Diode), LDR (Light Dependent Resistant) sensors, 3D printed sampling cartridge, and Arduino Mega to create a spectrophotometer with automation software to select wavelength, calculate calibration parameters, and report the concentration of the materials being studied. Spectrophotometers are finding applications across many fields, including clinical laboratories, pharmaceuticals, molecular biology, biochemistry, and even the medical realm. This study assembled an Arduino mega system coupled with software for the automatic selection of best-fit color light, calibration curve, and actual concentration interpolation processes. Dilution solutions from multiple chemical materials were evaluated to examine their linearity of calibration curves. Our study demonstrated that the tested chemical samples revealed a significant regression coefficient of up to 0.9945 for serial dilution solutions. Relative concentrations were measured with remarkable precision, exhibiting less than 5.0 % error compared to a commercial spectrophotometer. These findings highlight the viability of the RGB spectrophotometer and emphasize its potential with the promise of further refinement in development.

Photometric analysis of Type Ia Supernova 2023jvj

The study of supernovae – large explosions at the end of a star’s life that release immense energy and various elements into space – is important in the fields of stellar evolution and the expansion of the universe. In the large cosmological time scale, supernovae allow for a short glimpse into the universe’s evolution. Upon the discovery of Supernova (SN) 2023jvj, located in an anonymous galaxy in the Boötes constellation, our team launched an investigation to further knowledge of transient objects and their role in the cosmos. In this study, we conducted a photometric analysis of SN 2023jvj to determine its type and distance. We hypothesized SN 2023jvj would be a Type Ia supernova based on reports that there were no hydrogen spectral lines. We observed SN 2023jvj and collected photometric data over four weeks using the 12-inch Meade Schmidt Cassegrain Telescope (SCT) and 16-inch Ritchey-Chretien Telescope (RCT) at the Leitner Family Observatory and Planetarium (LFOP). Images were additionally collected remotely from Australia and Utah via the iTelescope Network. Using this data, standard Sloan green and Sloan red magnitudes were found through color calibration and used to generate a light curve (a plot of supernova brightness over time). SN 2023jvj was then determined to be a Type Ia supernova — a standard candle — with a distance modulus of 35.36. Therefore, SN 2023jvj is approximately 1.246e8 parsecs away from Earth. Our findings have a broader impact on how astronomers analyze the relationship between the brightness and distance of transient objects in the ever-expanding universe.

Machine Learning Evaluation of Fruit Ripeness with Multidimensional Sparse-Dataset Calibration

HighlightsThis article provides a machine-learning method for estimation of sugar content in target fruit using color appearance.Complex networks based on multidimensional datasets work effectively for color sensor calibration and data rebalancing.Beyond datapoint sparsity in datasets, estimation accuracy in sugar content is assured when an unsupervised-learning algorithm classifies the multidimensional datapoints composed of fruit color and relevant quantities.Abstract. In agriculture, harvests of fruits and vegetables depend on their ripeness, in which farmers should rely not only on the eye-sensed outlook but rigorously on constituents inside the targets, like sugar content. So far, detail measurements on the outlook color of fruits have been widely reported to detect suitable harvest times, where non-destructive instruments, which are costly in general agriculture fields, work well for color estimation whose resolution level is much higher than human eyes. In this study, we propose a scheme with a calibration procedure for sugar content prediction from outlook-color quantities, which is applicable even if a sensor includes systematic errors in output signals or the base dataset includes some datapoint sparsity. Most multidimensional data, which are both in agriculture and in other general cases, are imperfect in terms of several aspects, but they can be useful when we take appropriate sparsity detection and setting of rebalancing weights into account for machine learning procedures. We newly introduce a complex-network method for this purpose, and after the color calibration, we successfully obtain accurate hue and chroma, which are quantities representing a position on calibrated color coordinate, to predict °Brix sugar content in Japanese pears. Keywords: Asian pear, Color Elements, Fruit Ripening, °Brix Sugar Content, OHD.

Effect of primary peak wavelength and stimulus size on metameric failure through color difference evaluations

AbstractTristimulus values and chromaticities, which are derived using the color matching functions (CMFs), are commonly used for color characterization, calibration, and specifications, with the stimuli having the same values believed to have the same color appearance (i.e., metameric match). Many studies, however, found that the stimuli having the same tristimulus values do not appear the same (known as metameric failure) due to the failure of CMFs in accurately characterizing the color matching mechanisms. Most past work investigated the performance of different CMFs through color matching experiments, with a smaller chromaticity or calculated color difference between the reference and test stimuli suggesting a better performance. Such differences, however, may not accurately characterize the performance of the CMFs, since the color spaces or chromaticity diagrams may not be uniform, in terms of the threshold of noticeable color difference. In this study, the human observers evaluated the perceived color difference between pairs of stimuli, which were calibrated to have the same tristimulus values calculated using the CIE 1931 2° CMFs, with two sizes of field of view (FOV). The results clearly suggested that the stimuli having the same tristimulus values may not always appear the same, which depended on the primaries and the FOV. More importantly, the results clearly suggested that the chromaticity differences from color matching experiments may overestimate the metameric failure, especially when the color differences were small.

Color calibration of moist soil images captured under irregular lighting conditions

Soil color is a valuable indicator that visually reflects soil properties. In the field of soil science, soil color has been widely used for soil identification and property assessment. Soil color is influenced not only by soil properties but also by lighting conditions; therefore, adequately considering the effect of lighting conditions for the color-based soil assessment is essential. This paper proposes a color calibration method for moist soil images captured under irregular lighting conditions. Four samples of weathered granite soil with varying water contents were captured under nine different lighting conditions. The effect of lighting conditions on the color of moist soil was analyzed using linear regression analysis in the CIELAB color space. The findings demonstrate a linear relationship between soil color and lighting conditions. Furthermore, the slopes of the linear regression equations remained consistent across all moist soil samples, irrespective of soil type and water content. Based on this, calibration equations were derived to adjust the color of moist soil images captured under arbitrary lighting conditions to those under the desired lighting conditions. The proposed method was validated using soil images captured under natural lighting conditions, which show high accuracy in color calibration except for cases with illuminance exceeding 70,000 lux. This color calibration method provides a reliable approach to evaluating soil properties through accurate color representation and presents new possibilities for developing digital image-based soil property prediction techniques that are less sensitive to lighting conditions.

Comparison of four different displays for identification of select pathologic features extracted from whole slide images of surgical pathology cases

BackgroundThe establishment of minimum standards for display selection for the whole slide image (WSI) interpretation has not been fully defined. Recently, pathologists have increasingly preferred using remote displays for clinical diagnostics. Our study aims to assess and compare the performance of three fixed work displays and one remote personal display in accurately identifying ten selected pathologic features integrated into WSIs. DesignHematoxylin and eosin-stained glass slides were digitized using Philips scanners. Seven practicing pathologists and three residents reviewed ninety WSIs to identify ten pathologic features using the LG, Dell, and Samsung and an optional consumer-grade display. Ten pathologic features included eosinophils, neutrophils, plasma cells, granulomas, necrosis, mucin, hemosiderin, crystals, nucleoli, and mitoses. ResultsThe accuracy of the identification of ten features on different types of displays did not significantly differ among the three types of “fixed” workplace displays. The highest accuracy was observed for the identification of neutrophils, eosinophils, plasma cells, granuloma, and mucin. On the other hand, a lower accuracy was observed for the identification of crystals, mitoses, necrosis, hemosiderin, and nucleoli. Participant pathologists and residents preferred the use of larger displays (>30″) with a higher pixel count, resolution, and luminance. ConclusionMost features can be identified using any display. However, certain features posed more challenges across the three fixed display types. Furthermore, the use of a remote personal consumer-grade display chosen according to the pathologists’ preference showed similar feature identification accuracy. Several factors of display characteristics seemed to influence pathologists’ display preferences such as the display size, color, contrast ratio, pixel count, and luminance calibration. This study supports the use of standard “unlocked” vendor-agnostic displays for clinical digital pathology workflow rather than purchasing “locked” and more expensive displays that are part of a digital pathology system.

Minimum error adaptive RGB calibration in a context of colorimetric uncertainty for cultural heritage preservation

In this paper, two aims are sought. First, the path to a minimum-error colour calibration and its feasibility for non-invasive cultural heritage conservation is explored. Using RGB information as temporal cues of the conservation state of the pieces under monitoring, an adaptive calibration function, working in syntony with a colour calibration chart destined to general usage, has been proposed. Since the context involves citizen participation, the pictures to calibrate are crowdsourced and the cameras of origin unknown; therefore, the requirements of the work context assume images that imply a heavy colorimetric uncertainty whilst maintaining their structural content. Therefore,a differentiable, multidimensional, adaptive transfer curve that offers the best trade-off between calibration error (staying in minimum perceivable colour differences for tangible materials) and calculation requirements is refined. The conception of this system offers flexible possibilities of an effective cultural heritage conservation without heavy costs on money and human effort or relying on external solutions, while standing on par in quality with state-of-art technologies, achieving therefore a balance between generalist usage and specific solutions. Secondly, a neural network-based final process performs the final tuning of the calibration functions, and its conclusions shed light on how a calibration process works together with the colour chart on an algebraic level. The verification of this phenomenon opens possibilities of different paths on how to proceed with colour calibrations depending on the conditions of acquisition and the content of interest, so the minimum error can be always achieved regardless of the case. The ideas presented here are an expansion and conclusion of the developments and results previously published by the same authors.