Color Consistency Research Articles

Research on Color Management and Color Grading Applications in New Media Movies

This article studies the color management and color grading of new media movies, mainly studying the impact of color balance, stylized color grading and high dynamic range image adjustment on the visual effects and emotional expression of the film. However, there are currently not many specifications and requirements for the color requirements of new media movies. New media movies still have deficiencies in color aesthetics and artistry. They often pursue visual stimulation and alternative themes while ignoring artistic expression. Due to limitations in cost and production experience, online movies often have less investment in color aesthetics, and the production level is still far behind traditional movies. The research significance of this article is to emphasize the importance of color management and color correction technology in new media movies, and to propose solutions to color inconsistency problems caused by color management and cross-platform playback. Through scientific color management and delivery settings, new media movies can be ensured to present the best visual effects on different devices, improving the artistic value and enjoyment of new media movies. In general, the research in this article has reference significance for the standardization development and technological improvement of the new media film industry.

A Study on Enhancing the Visual Fidelity of Aviation Simulators Using WGAN-GP for Remote Sensing Image Color Correction

When implementing outside-the-window (OTW) visuals in aviation tactical simulators, maintaining terrain image color consistency is critical for enhancing pilot immersion and focus. However, due to various environmental factors, inconsistent image colors in terrain can cause visual confusion and diminish realism. To address these issues, a color correction technique based on a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is proposed. The proposed WGAN-GP model utilizes multi-scale feature extraction and Wasserstein distance to effectively measure and adjust the color distribution difference between the input image and the reference image. This approach can preserve the texture and structural characteristics of the image while maintaining color consistency. In particular, by converting Bands 2, 3, and 4 of the BigEarthNet-S2 dataset into RGB images as the reference image and preprocessing the reference image to serve as the input image, it is demonstrated that the proposed WGAN-GP model can handle large-scale remote sensing images containing various lighting conditions and color differences. The experimental results showed that the proposed WGAN-GP model outperformed traditional methods, such as histogram matching and color transfer, and was effective in reflecting the style of the reference image to the target image while maintaining the structural elements of the target image during the training process. Quantitative analysis demonstrated that the mid-stage model achieved a PSNR of 28.93 dB and an SSIM of 0.7116, which significantly outperforms traditional methods. Furthermore, the LPIPS score was reduced to 0.3978, indicating improved perceptual similarity. This approach can contribute to improving the visual elements of the simulator to enhance pilot immersion and has the potential to significantly reduce time and costs compared to the manual methods currently used by the Republic of Korea Air Force.

Calcifying epithelial odontogenic tumour with an atypical clinical presentation in a paediatric patient: A case report and literature review

AbstractAimWe report a rare case of a calcifying epithelial odontogenic tumour with atypical clinical presentation in an adolescent and present a literature review of this tumour in paediatric patients.Materials and MethodsA 12‐year‐old female patient presented with a painless swelling of the left mandible, with a history of 6 months. Clinical examination revealed a sessile mass with a reddish‐pink colour and firm consistency. Tomographic images showed a hypodense, unilocular lesion involving unerupted tooth #34, with a hyperdense focus in contact with the tooth crown disrupting the buccal cortex.ResultsEnucleation, curettage and peripheral osteotomy of the lesion were performed, and the impacted tooth was removed, followed by the application of autologous leucocyte and platelet‐rich fibrin matrices to the region. The patient evolved favourably with good clinical healing and evidence of new bone formation.ConclusionsDespite its aggressive potential, the conservative approach for CEOT in paediatric patients proved to be effective. Our review found reports of recurrence up to 13 years after surgery. Therefore, long‐term clinical and radiographical follow‐up is indicated.

Bilateral fusion low‐light image enhancement with implicit information constraints

AbstractResearch on low‐light image enhancement focuses on improving image quality in dim conditions. Recently, deep learning has driven significant advancements, with many studies using neural networks to enhance low‐light images. However, most focus on complex network designs to increase nonlinearity, often neglecting the implicit information from local image transformations. This paper introduces an improved U‐net‐based method for low‐light enhancement, retaining the original encoding network and adding branch links in the decoding network. The method uses attention feature fusion to handle image noise and gradients separately, adjusting brightness through a gradient‐adaptive transform. This approach optimizes performance using loss functions like peak signal‐to‐noise ratio and colour consistency. Unlike previous methods, the approach emphasizes extracting implicit information from image gradients, achieving enhancement that aligns with the original brightness distribution. The result is enhanced images with high detail similarity to the original, achieved through end‐to‐end inference in experiments.

Evaluasi dan Redesain UI/UX pada Aplikasi Web Young on Top

The youth community plays a vital role in supporting the career development of young people. In this context, the user interface (UI) and user experience (UX) of the Young On Top web application play a crucial role. The app experienced UI/UX-related challenges, such as issues with image size, information completeness, and layout inconsistencies, which directly impacted user confusion. The research method used involved a usability evaluation with the System Usability Scale (SUS) as well as interviews with participants, both new and experienced users. The redesign focused on improving the layout, color consistency, and adding important information on various pages such as registration, events, career, shop, and articles. These changes significantly improved the appearance and user experience. This research emphasizes the importance of evaluation and improvement in UI/UX to improve the quality and user satisfaction of the Young On Top web application. Thus, the alignment between users' needs and ease of interaction with the application can be optimally realized.

New color channel driven physical lighting model for low-light image enhancement

Outdoor imaging systems, affected by low-light conditions, generally produce low-quality images with poor visibility. Low-quality images can directly influence high-level tasks such as surveillance and autonomous navigation systems. Enhancing the images captured under inadequate lighting conditions aims to generate higher visual quality in these images. However, current low-light enhancement methods may result in color unnaturalness, information loss, and strange artifacts. We propose a new color channel-driven physical lighting model (NCC-PLM) to respond to these issues to improve image quality. More concretely, we first apply a gamma correction to the input image according to its darkness degree, which is determined by its average intensity value. Then, we introduce a new color channel prior to estimate the environmental light (EL) and light scattering attenuation rate (LSAR). Finally, the enhanced image is obtained through the estimations and physical lighting model. Experimental results on various datasets demonstrate the proposed method's effectiveness and superiority over the compared methods both visually and qualitatively. Specifically, we enhance the visual quality of low-light images by revealing intricate details and maintaining color consistency, leading to a natural appearance.

Impacts of Vitis coignetiae Pulliat extract on the physicochemical properties in dry-fermented sausages during storage

The present study was designed to compare different extraction methods in meoru (Vitis coignetiae Pulliat). The effect of meoru extract on the quality characteristics of dry-fermented sausages was also evaluated. Ground pork sausages were assigned to different groups: control without meoru hot water extract, M1 (with 0.01% meoru hot water extract), and M2 (with 0.02% meoru hot water extract). Antioxidant yield, phenolic compound content, and antioxidant properties such as 2,2-diphenyl-1-picrylhydrazyl, 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid, and ferric-reducing antioxidant power were highest in groups with the meoru hot water extract. Both groups with meoru extract had significantly lower pH and moisture content at all storage times compared with the control. The inclusion of 0.2% meoru in sausages resulted in similar meat color values compared with the control. The rate of lipid oxidation significantly decreased in the groups with meoru extract addition. When the amount of meoru extract was increased to 0.02%, lactic acid bacteria was significantly enhanced. Therefore, 0.02% meoru addition using the hot water extraction method showed effective antioxidant performance, increased lactic acid bacteria, and color consistency in dry-fermented sausages during cold storage.

A Texture-Considerate Convolutional Neural Network Approach for Color Consistency in Remote Sensing Imagery

Remote sensing allows us to conduct large-scale scientific studies that require extensive mapping and the amalgamation of numerous images. However, owing to variations in radiation, atmospheric conditions, sensor perspectives, and land cover, significant color discrepancies often arise between different images, necessitating color consistency adjustments for effective image mosaicking and applications. Existing methods for color consistency adjustment in remote sensing images struggle with complex one-to-many nonlinear color-mapping relationships, often resulting in texture distortions. To address these challenges, this study proposes a convolutional neural network-based color consistency method for remote sensing cartography that considers both global and local color mapping and texture mapping constrained by the source domain. This method effectively handles complex color-mapping relationships while minimizing texture distortions in the target image. Comparative experiments on remote sensing images from different times, sensors, and resolutions demonstrated that our method achieved superior color consistency, preserved fine texture details, and provided visually appealing outcomes, assisting in generating large-area data products.

The Semiotic Role of Color in Alcoholic Beverage Packaging and Its Advertising Synergy

This study explores the semiotic role of color in alcoholic beverage packaging and its synergy with advertising campaigns. Through a mixed-methods approach involving content analysis, case studies, and consumer surveys, we examine how color choices in packaging influence consumer perception, brand identity, and marketing effectiveness. This finding reveal that specific colors evoke distinct psychological responses and cultural meanings, which significantly impact brand recognition and consumer loyalty. The research highlights successful strategies used by brands such as Absolut Vodka, Coca-Cola, and Heineken, demonstrating the importance of color consistency across packaging and advertising in creating a cohesive and memorable brand image. Additionally, this study discusses the role of digital advertising in maintaining color synergy and the need for cultural adaptation in global marketing. This study provides valuable insights for brand managers and marketers, emphasizing the strategic use of color to enhance visual identity, brand recall, and consumer engagement.

An Automatic Detection and Statistical Method for Underwater Fish Based on Foreground Region Convolution Network (FR-CNN)

Computer vision in marine ranching enables real-time monitoring of underwater resources. Detecting fish presents challenges due to varying water turbidity and lighting, affecting color consistency. We propose a Foreground Region Convolutional Neural Network (FR-CNN) that combines unsupervised and supervised methods. It introduces an adaptive multiscale regression Gaussian background model to distinguish fish from noise at different scales. Probability density functions integrate spatiotemporal information for object detection, addressing illumination and water quality shifts. FR-CNN achieves 95% mAP with IoU of 0.5, reducing errors from open-source datasets. It updates anchor boxes automatically on local datasets, enhancing object detection accuracy in long-term monitoring. The results analyze fish species behaviors in relation to environmental conditions, validating the method’s practicality.

Ancient paintings inpainting based on dual encoders and contextual information

Deep learning-based inpainting models have achieved success in restoring natural images, yet their application to ancient paintings encounters challenges due to the loss of texture, lines, and color. To address these issues, we introduce an ancient painting inpainting model based on dual encoders and contextual information to overcome the lack of feature extraction and detail texture recovery when restoring ancient paintings. Specifically, the proposed model employs a gated encoding branch that aims to minimize information loss and effectively capture semantic information from ancient paintings. A dense multi-scale feature fusion module is designed to extract texture and detail information at various scales, while dilated depthwise separable convolutions are utilized to reduce parameters and enhance computational efficiency. Furthermore, a contextual feature aggregation module is incorporated to extract contextual features, enhancing the overall consistency of the inpainting results. Finally, a color loss function is introduced to ensure color consistency in the restored area, harmonizing it with the surrounding region. The experimental results indicate that the proposed model effectively restores the texture details of ancient paintings, outperforming other methods both qualitatively and quantitatively. Additionally, the model is tested on real damaged ancient paintings to validate its practicality and efficacy.

Towards a Flexible Semantic Guided Model for Single Image Enhancement and Restoration.

Low-light image enhancement (LLIE) investigates how to improve the brightness of an image captured in illumination-insufficient environments. The majority of existing methods enhance low-light images in a global and uniform manner, without taking into account the semantic information of different regions. Consequently, a network may easily deviate from the original color of local regions. To address this issue, we propose a semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that adaptively integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in the LLIE task. We further present a refined framework SKF++ with two new techniques: (a) Extra convolutional branch for intra-class illumination and color recovery through extracting local information and (b) Equalization-based histogram transformation for contrast enhancement and high dynamic range adjustment. Extensive experiments on various benchmarks of LLIE task and other image processing tasks show that models equipped with the SKF/SKF++ significantly outperform the baselines and our SKF/SKF++ generalizes to different models and scenes well. Besides, the potential benefits of our method in face detection and semantic segmentation in low-light conditions are discussed. The code and pre-trained models have been publicly available at https://github.com/langmanbusi/Semantic-Aware-Low-Light-Image-Enhancement.

DBMKA-Net:Dual branch multi-perception kernel adaptation for underwater image enhancement

In recent years, due to the dependence on wavelength-based light absorption and scattering, underwater photographs captured by devices often exhibit characteristics such as blurriness, faded color tones, and low contrast. To address these challenges, convolutional neural networks (CNNs) with their robust feature-capturing capabilities and adaptable structures have been employed for underwater image enhancement. However, most CNN-based studies on underwater image enhancement have not taken into account color space kernel convolution adaptability, which can significantly enhance the model’s expressive capacity. Building upon current academic research on adjusting the color space size for each perceptual field, this paper introduces a Double-Branch Multi-Perception Kernel Adaptive (DBMKA) model. A DBMKA module is constructed through two perceptual branches that adapt the kernels: channel features and local image entropy. Additionally, considering the pronounced attenuation of the red channel in underwater images, a Dependency-Capturing Feature Jump Connection module (DCFJC) has been designed to capture the red channel’s dependence on the blue and green channels for compensation. Its skip mechanism effectively preserves color contextual information. To better utilize the extracted features for enhancing underwater images, a Cross-Level Attention Feature Fusion (CLAFF) module has been designed. With the Double-Branch Multi-Perception Kernel Adaptive model, Dependency-Capturing Skip Connection module, and Cross-Level Adaptive Feature Fusion module, this network can effectively enhance various types of underwater images. Qualitative and quantitative evaluations were conducted on the UIEB and EUVP datasets. In the color correction comparison experiments, our method demonstrated a more uniform red channel distribution across all gray levels, maintaining color consistency and naturalness. Regarding image information entropy (IIE) and average gradient (AG), the data confirmed our method’s superiority in preserving image details. Furthermore, our proposed method showed performance improvements exceeding 10% on other metrics like MSE and UCIQE, further validating its effectiveness and accuracy.

Semantic attention guided low-light image enhancement with multi-scale perception

Low-light environments often lead to complex degradation of captured images. However, most deep learning-based image enhancement methods for low-light conditions only learn a single-channel mapping relationship between the input image in low-light conditions and the desired image in normal light without considering semantic priors. This may cause the network to deviate from the original color of the region. In addition, deep network architectures are not suitable for low-light image recovery due to low pixel values. To address these issues, we propose a novel network called SAGNet. It consists of two branches:the main branch extracts global enhancement features at the level of the original image, and the other branch introduces semantic information through region-based feature learning and learns local enhancement features for semantic regions with multi-level perception to maintain color consistency. The extracted features are merged with the global enhancement features for semantic consistency and visualization. We also propose an unsupervised loss function to improve the network’s adaptability to general scenes and reduce the effect of sparse datasets. Extensive experiments and ablation studies show that SAGNet maintains color accuracy better in all cases and keeps natural luminance consistency across the semantic range.

Comparison of Chromatic Assimilation Effects Depending on Printing Substrate in the Munker-White Model

Understanding the influence of paper surface structures on color perception is crucial for the printing industry. This research investigates the impact of horizontal and vertical parallel lines on chromatic assimilation using the Munker-White grid model. The study employs the perceived ∆E00 metric to quantify color differences and determine whether these textures affect perceived color accuracy and consistency differently. Results reveal variations in color perception due to surface textures. The biggest color difference (ΔE00) occurred at 60% RTV coverage for cyan, where the vertical structure showed a significantly lower value than the horizontal. For the yellow primary stimulus, the greatest difference was observed at 20% RTV coverage, with both structures showing high initial values that decrease with increased coverage. These findings provide valuable insights for improving color management practices and enhancing the quality and reliability of color reproduction on various substrates, contributing to advancements in printing technology and color science.

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.

Stain SAN: simultaneous augmentation and normalization for histopathology images.

We address the need for effective stain domain adaptation methods in histopathology to enhance the performance of downstream computational tasks, particularly classification. Existing methods exhibit varying strengths and weaknesses, prompting the exploration of a different approach. The focus is on improving stain color consistency, expanding the stain domain scope, and minimizing the domain gap between image batches. We introduce a new domain adaptation method, Stain simultaneous augmentation and normalization (SAN), designed to adjust the distribution of stain colors to align with a target distribution. Stain SAN combines the merits of established methods, such as stain normalization, stain augmentation, and stain mix-up, while mitigating their inherent limitations. Stain SAN adapts stain domains by resampling stain color matrices from a well-structured target distribution. Experimental evaluations of cross-dataset clinical estrogen receptor status classification demonstrate the efficacy of Stain SAN and its superior performance compared with existing stain adaptation methods. In one case, the area under the curve (AUC) increased by 11.4%. Overall, our results clearly show the improvements made over the history of the development of these methods culminating with substantial enhancement provided by Stain SAN. Furthermore, we show that Stain SAN achieves results comparable with the state-of-the-art generative adversarial network-based approach without requiring separate training for stain adaptation or access to the target domain during training. Stain SAN's performance is on par with HistAuGAN, proving its effectiveness and computational efficiency. Stain SAN emerges as a promising solution, addressing the potential shortcomings of contemporary stain adaptation methods. Its effectiveness is underscored by notable improvements in the context of clinical estrogen receptor status classification, where it achieves the best AUC performance. The findings endorse Stain SAN as a robust approach for stain domain adaptation in histopathology images, with implications for advancing computational tasks in the field.

Fast color fourier Ptychographic microscopic imaging technology with fusion color correction

In biomedicine, rapid and accurate acquisition of color pathology slides is of paramount importance for diagnostic analyses. Conventional microscopic full-color imaging cannot provide the physician with enough effective information at one time due to the "law of proportionality of objective lenses." Fourier ptychographic microscopy (FPM) has emerged as a solution for high-throughput digital pathology. It can simultaneously achieve a large field of view and high-resolution (HR) imaging. However, achieving color FPM typically requires capturing images separately for each of the red, green, and blue channels for reconstruction, resulting in a significant increase in the time cost. Existing improvement strategies require long coloring times, and have difficulty ensuring standardization between the reconstructed and original image colors owing to the influence of optical system stability and algorithm errors. In this study, a fast-color FPM imaging technology with fusion color correction (CCFPM) is proposed. It achieves high-precision color FPM imaging by separately capturing single low-resolution images for the red and blue channels, which are used to color the reconstructed green-channel HR grayscale image. The reconstructed HR color image is then processed using a color correction algorithm. Simulation and experimental analyses demonstrate that this method compresses the coloring algorithm's computation time to less than 1 s, reducing both the acquisition and reconstruction times by 66.7 % compared with the traditional R + G + B channel approach, achieving a significant improvement in speed. Meanwhile, it also achieves a breakthrough in algorithm speed compared with other fast color FPM solutions, compressing the coloring time to a few hundredths or even a few thousandths of the existing algorithms, and ensures the accuracy of the reconstructed image, reducing the RMSE by 50 % compared with the existing algorithms. Regarding color consistency, CCFPM can mitigate the chromatic aberration caused by fluctuations in optical system stability, maintaining consistency between the reconstructed and original image colors, thus achieving standardization of image color appearance. Therefore, the proposed method is promising for further advancements in the application of FPM in digital pathology.

Large Scale and Complex Structure Grotto Digitalization Using Photogrammetric Method: A Case Study of Cave No. 13 in Yungang Grottoes

Abstract. 3D reconstruction of cultural heritage with large volume and high precision is a technical problem in the field of photogrammetry. This paper studies a high-precision digitalization method for large-volume immovable heritage assets based on photogrammetry and laser scanning. It solves the problem of large-scale aerial triangulation and ensures overall color and geometric consistency while satisfying high-precision modeling of local details. Taking the millimeter accuracy 3D reconstruction project of Cave No. 13 in Yungang Grottoes as an example, we use more than 280,000 arbitrary images to reconstruct the entire cave and verify the effectiveness of the proposed method.

Forensic characteristics of injuries from thermo-baric explosive device

Since the beginning of the Russian Federation's invasion of Ukraine in 2022, explosive trauma has become an extremely urgent problem, as the main source of bodily injury among both the military in the combat zone and the civilian population in cities has been the impact of explosive devices. The aim of the study is the examination of the forensic characteristics of damage to biological objects that were formed from thermal exposure and shock wave as a result of the explosion of a cumulative munition and in the conditions of an experimental explosion model. The objects of the study were the materials of two examinations on the death of Ukrainian soldiers who died in the war zone (archival "Conclusions of the medical examination" of the Kyiv City Clinical Bureau of Forensic Medical Examination in 2023). Under the conditions of the experiment, studies of pathomorphological changes in the liver and small intestine of 30 white outbred rats from the action of an artificially created air shock wave with an overpressure of 31.62±4.84 kPa were carried out. The injuries were examined macroscopically and using standard laboratory histological techniques. Microscopy of histological sections was performed using an Axio Imager 2 microscope (Zeiss, Germany) at magnifications of ×200 and ×400. Statistical processing of the obtained quantitative results was carried out using the STATISTICA 6.1 software product. Under the condition of the explosion of the ammunition with the cumulative effect of the rocket-propelled infantry flamethrower "Bumblebee" on sectional incisions of the skin and muscles of the thigh in the projection of areas of redness, a picture of a gelatinous consistency of bright red color was macroscopically determined due to abundant blood impregnation of muscles and subcutaneous fat and partial loss of muscle structure with the release of myoglobin. The bright red color of the skin of the thigh and pelvis without burning the hair may indicate the superficial thermal effect of the explosive device and the protection of the skin by clothing. Diffusely located numerous both paired and single abrasions and shallow wounds, small rounded, oblong, circular in shape, which are the result of fragments of a rocket-propelled grenade equipped with a fire mixture, were also determined. The effect of an air shock wave with an overpressure of 31.62±4.84 kPa on the liver parenchyma of rats was determined by focal hemorrhages with rupture of the terminal central vein of the hepatic lobule, edema of the parasinusoidal spaces, and sludges in the sinusoids. In the wall of the small intestine, acute hemodynamic disorders occurred in the form of vasodilation of arterial vessels, venular and capillary stasis. There was layering and swelling of the small intestine wall, rupture of veins, focal hemorrhage. Thus, the revealed characteristic pathomorphological signs of the destructive effect of overpressure as a result of a blast wave (barotrauma) are typical and common both in the areas of the human thigh and pelvis, and in biological objects of experimental animals. The obtained results are consistent with the pathomorphological manifestations of barotrauma in areas of the human body as a result of the action of an explosive device with a cumulative effect.