Color Style Research Articles

Selection of optimal display color for China’s emergency management system using eye tracking

In recent years, eye-tracking analysis has been widely applied in both basic and applied research fields, yielding notable results. However, studies on specific scenarios, such as the display styles of emergency rescue platforms, are still lacking. China’s vast territory encompasses diverse climates and natural environments. To address the various environmental challenges, an emergency management digital twin system platform has been developed to enable real-time safety monitoring of key industries and high-risk areas for frequent accidents. However, the display style of the platform, particularly the color scheme, lacks uniformity. Currently, there are five different color themes widely used across various levels of the emergency management system: “Tech Style”, “Blue”, “Bright Blue”, “Transparent Gray” and “Green”. To explore the impact of color styles on information search efficiency in the existing emergency management platform, this study collects eye-tracking data from 10 participants while searching for information under five different color styles. The data are then subjected to feature extraction and statistical analysis in terms of time, frequency, and range. The statistical test results indicate that different color styles significantly affect the efficiency of information searches on the platform, and the analysis results are highly consistent with the findings of user studies. Research results indicate that the “Transparent Gray” theme outperforms the other themes in terms of search efficiency and user subjective evaluations, with the “Tech Style” theme following closely behind. This research is expected to provide a foundation for standardizing the color themes of China’s emergency management platform and contribute to the scientific and rational design of global emergency management platforms.

Unsupervised content and style learning for multimodal cross-domain image translation

Recently, impressive progress has been made in cross-domain image translation using image generation models pre-trained on massive amounts of data since these pre-trained generative models have strong generative capabilities.However, due to the fact that these pre-trained generative models are usually generated based on the principle of diffusion, there are often the following issues, such as color pattern distortion and the inability to maintain content structure during the translation process. Existing methods fail to meet the requirements of cross-domain image translation, where the shape (content structure) of the image should remain unchanged or undergo minimal change. To address these challenges, we propose an unsupervised content and style learning method for cross-domain image translation. Our approach utilizes self-structure attention loss to preserve content structure and color constraint loss to map the color space of the reference image to the translated image. This effectively separates the image’s content structure from its color style, enabling high-quality multi-modal cross-domain image translation. Experiments on multiple datasets demonstrate that our method outperforms state-of-the-art approaches in metrics such as LPIPS, NDB, JS, IS, and excels in maintaining content structure and color patterns.

Improving the generalizability of white blood cell classification with few-shot domain adaptation

The morphological classification of nucleated blood cells is fundamental for the diagnosis of hematological diseases. Many Deep Learning algorithms have been implemented to automatize this classification task, but most of the time they fail to classify images coming from different sources. This is known as “domain shift”. Whereas some research has been conducted in this area, domain adaptation techniques are often computationally expensive and can introduce significant modifications to initial cell images. In this article, we propose an easy-to-implement workflow where we trained a model to classify images from two datasets, and tested it on images coming from eight other datasets. An EfficientNet model was trained on a source dataset comprising images from two different datasets. It was afterwards fine-tuned on each of the eight target datasets by using 100 or less-annotated images from these datasets. Images from both the source and the target dataset underwent a color transform to put them into a standardized color style. The importance of color transform and fine-tuning was evaluated through an ablation study and visually assessed with scatter plots, and an extensive error analysis was carried out. The model achieved an accuracy higher than 80% for every dataset and exceeded 90% for more than half of the datasets. The presented workflow yielded promising results in terms of generalizability, significantly improving performance on target datasets, whereas keeping low computational cost and maintaining consistent color transformations. Source code is available at: https://github.com/mc2295/WBC_Generalization

A Comprehensive Method for Example-Based Color Transfer with Holistic-Local Balancing and Unit-Wise Riemannian Information Gradient Acceleration.

Color transfer, an essential technique in image editing, has recently received significant attention. However, achieving a balance between holistic color style transfer and local detail refinement remains a challenging task. This paper proposes an innovative color transfer method, named BHL, which stands for Balanced consideration of both Holistic transformation and Local refinement. The BHL method employs a statistical framework to address the challenge of achieving a balance between holistic color transfer and the preservation of fine details during the color transfer process. Holistic color transformation is achieved using optimal transport theory within the generalized Gaussian modeling framework. The local refinement module adjusts color and texture details on a per-pixel basis using a Gaussian Mixture Model (GMM). To address the high computational complexity inherent in complex statistical modeling, a parameter estimation method called the unit-wise Riemannian information gradient (uRIG) method is introduced. The uRIG method significantly reduces the computational burden through the second-order acceleration effect of the Fisher information metric. Comprehensive experiments demonstrate that the BHL method outperforms state-of-the-art techniques in both visual quality and objective evaluation criteria, even under stringent time constraints. Remarkably, the BHL method processes high-resolution images in an average of 4.874 s, achieving the fastest processing time compared to the baselines. The BHL method represents a significant advancement in the field of color transfer, offering a balanced approach that combines holistic transformation and local refinement while maintaining efficiency and high visual quality.

Allium taiseba, a new species of Allium sect. Scorodon (Amaryllidaceae) from Van province (Turkey)

A new species, Allium taiseba (A. sect. Scorodon), is described from Hoşap (Xoşap)/Van province, Turkey. It is morphologically similar to A. bingoelense, but it differs in several morphological features including inner tunic's colour of bulb, spathe length, tepal colours and size, stamen colour and length, ovary shape, style colour and length, capsule and seeds size. A comprehensive description of the new species is provided, including detailed photographs, geographical distribution map, habitat and ecology, vernacular names and IUCN conservation status.

Amorphophallus samarensis (Araceae), a new species endemic to Samar Island, Eastern Visayas, Philippines

Amorphophallus samarensis is described as a new species from Paranas, Samar Island Natural Park, Samar Eastern Visayas, Philippines. A. samarensis resembles A. calcicola by having a solitary leaf, long peduncled solitary flower, cylindric female zone, ovary depressed, disk shape, slightly distant ovaries, and absence of staminode. It differs from A. calcicola by having the rachis winged at the distal part of the leaf, pale to dark maroon slightly depressed globose capitate stigma, style color, length of spadix, and truncate anther. The new species is considered Critically Endangered (CR) based on IUCN guidelines due to persistent anthropogenic activities. There are 30 individual plants encountered in the locality with less than 100 sq. km area of occupancy with an observed threat.

Application of visual attribute transfer technology in analysing changes in emotional expression in picture books

AbstractIn picture books, readers can obtain different emotional perceptions according to different image style attributes. Artists often use different combinations of colours, textures, materials, and other style elements in images to convey different emotions in their creations. Especially in picture books for children, there is a strong correlation between the perceived effect of the work and the accuracy and degree of emotional expression. In the process of creating picture books, various factors will affect the efficiency of artists trying to transfer styles to meet their creative needs. With the development of image style transfer technology based on a deep convolutional neural network, artists can use this technology to create works with different styles of emotional changes efficiently. In this paper, we select illustrations of picture books and use deep convolutional neural networks to transfer image styles from three aspects: colour style transfer, texture style, and material style transfer. Through sampling survey experiments, we discuss the changes in image attributes, emotional expression, and emotional perception in picture books for children. The survey results found that the most direct and evident influence on the emotional changes of picture book images is the transfer of colour style attributes, material style attributes, and texture style attributes. The results of this study can provide a valuable reference for improving the accuracy of emotional expression, the depth of meaning extension, and the height of artistic value in picture books for children during the process of an artist's creation. This research stands out by systematically analysing the distinct impact of each style attribute transfer, offering a comprehensive framework that can be utilized by artists and technologists alike to enhance the emotional and artistic quality of children's picture books.

A Study on the Meaning of Colors in Films: Taking Krzysztof Kieslowski’s The Blue, White, and Red Trilogy as an Example

As an important element of cinematic art, color not only aids in storytelling but also shapes the visual style of a film and reflects its themes and emotions. This paper aims to explore the meaning of Kieslowski’s use of color by analyzing the color styles in the The Blue, White, and Red Trilogy and examining how the symbolic meanings of these colors are conveyed. This article is divided into three parts, which are a deep analysis of the color symbols and meanings in the three films Blue, White, and Red. The theoretical analysis and practical exploration of the deep value meanings of freedom, equality, and universal love represented by the colors in the three films are carried out.

Thermal image visualization using multi-discriminator CycleGAN with unpaired thermal–visible image training set

Thermal imaging utilizes the differences in thermal radiation within a scene to capture images, effectively compensating for the limited imaging capabilities of visible-light cameras in low-light environments. In order to further enhance thermal imaging, we propose a novel approach that transforms the visualization of thermal images into an image conversion task. Our method, named TIVNet (Thermal Images Visualization Network), is based on a multi-discriminator cycle-consistent adversarial network (CycleGAN) and operates in the YUV color space to accurately colorize and represent thermal infrared images. The utilization of color and texture discriminators in our framework ensures that the converted images exhibit appropriate color styles and capture fine details, respectively. In the generator, we employed a Dense Residual Module (DRM) to facilitate the reuse of features and enhance the flow of information. By utilizing the DRM, we aim to enhance the overall performance and efficiency of the generator network. Additionally, we incorporated the Dual-channel Self-Attention Module(DCSA) to enhance the feature representation. By leveraging self-attention, the generator can effectively learn and exploit the inherent relationships between different regions, leading to improved feature representation and, consequently, more accurate colorization and representation of thermal infrared images. Notably, our proposed method leverages unsupervised learning, eliminating the need for a fully paired thermal infrared-visible image dataset. Through extensive experimentation on publicly available datasets as well as our own, we demonstrate that our approach outperforms state-of-the-art methods in thermal image visualization. Furthermore, the converted images produced by TIVNet exhibit a high level of realism, thereby enhancing the visual impact of thermal imaging. This improvement is particularly beneficial for human perception and subsequent target recognition tasks. Our findings suggest that the application of our method yields superior results both quantitatively and qualitatively, showcasing its potential for advancing thermal imaging capabilities.

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.

ChromaFusionNet (CFNet): Natural Fusion of Fine-Grained Color Editing

Digital image enhancement aims to deliver visually striking, pleasing images that align with human perception. While global techniques can elevate the image's overall aesthetics, fine-grained color enhancement can further boost visual appeal and expressiveness. However, colorists frequently face challenges in achieving accurate, localized color adjustments. Direct composition of these local edits can result in spatial color inconsistencies. Existing methods, including color style transfer and image harmonization, exhibit inconsistencies, especially at boundary regions. Addressing this, we present ChromaFusionNet (CFNet), a novel approach that views the color fusion problem through the lens of image color inpainting. Built on the Vision Transformer architecture, CFNet captures global context and delivers high-fidelity outputs, seamlessly blending colors while preserving boundary integrity. Empirical studies on ImageNet and COCO datasets demonstrate CFNet's superiority over existing methods in maintaining color harmony and color fidelity. Robustness evaluations and user studies have further validated the effectiveness of CFNet. In conclusion, CFNet introduces an innovative approach to seamless, fine-grained color fusion, paving the way for advancements in the domain of fine-grained color editing. Code and pretrained models are available at our project page: https://yidong.pro/projects/cfnet.

Optimal transport-based domain adaptation for semantic segmentation of remote sensing images

ABSTRACT Thanks to its great power in feature representation, deep learning (DL) is widely used in semantic segmentation tasks. However, the requirements for high distribution consistency of different domains are too tight to be met by large-scale remote sensing tasks due to the domain shift in imaging modes and geographic environments. In this case, trained models in a source domain can hardly achieve sufficient accuracy in a target domain with domain shift. To address this issue, a novel unsupervised domain adaptation (UDA) method driven by optimal transport (OT) with two-stage training is proposed to alleviate domain shift in remote sensing images (RSIs). In the first stage, a colour distribution alignment (CDA) module and a feature joint alignment (FJA) module based on OT were designed to mitigate the discrepancy between different domains. CDA transports source-domain distribution according to the target-domain colour style, and FJA aligns source and target domains in both feature and output spaces by minimizing OT-based losses. In the second stage, self-training with pseudo-label denoising (STPD) was proposed, which alleviated the interference of noises in pseudo-labels based on a joint OT distance. For the experiments, the Potsdam, Vaihingen and UAVid datasets were employed. Based on the characteristics of these datasets, five UDA tasks were introduced. The results of these UDA experiments indicate the superiority of our method. Our code will be available at https://github.com/Hcshenziyang/OT-Domain-Adaptation-Semantic-Segmentation.

Research and application of art design based on visual communication technology

Abstract This paper deeply discusses the art design based on visual communication technology, studies its application in modern art expression, and its influence and promotion on design aesthetics. The research starts from three aspects: the hierarchical needs of art design, the relationship between visual communication and art design expression and the integration path of art design and visual communication. Through the hierarchical analysis of art design expression content, the application of media design, composition design and metaphor design in visual art is discussed in depth. Meanwhile, the relationship between art design and visual communication is studied and the path of integration between art design and visual communication is proposed. The experimental part includes the extraction of art design style features, the recognition of artworks based on color style and texture features, and the satisfaction analysis of art design effects. The results show that the method proposed in this study has high accuracy in art style recognition, excellent performance in art texture recognition, and good feedback regarding satisfaction with art design effects. This proves the effective application of visual communication technology in art design, which is significant in promoting modern art design development.

Sustainable and green process for recycling waste wool textiles into high-quality protein fibers on a pilot scale

For the first time, waste wool textiles have been recycled into high-quality protein fibers via protein dissolution. Traditional mechanical wool recycling, with serious damage to protein fibers and limited color styles on fibers, only boosts 5% of post-consumer wool recycling rate. Other recycling is hindered due to the failure of dye removal and protein dissolution. Here, we have developed green and sustainable processes of complete dye separation, establishment of desirable molecular entanglement in protein dissolution, and recovery of crosslinkages in spun fibers. More than 80% of waste wool was recycled into protein fibers, which were comparable to virgin wool fibers, with retention of more than 90% of mechanical, wet, and dyeing properties of virgin wool. Recycled dyes, completely separated from proteins via the disruption of molecular interactions in wool and controlled protein precipitation, were reused and demonstrated the desired dyeability without generating dye wastes.

Reference-Based Deep Line Art Video Colorization.

Coloring line art images based on the colors of reference images is a crucial stage in animation production, which is time-consuming and tedious. This paper proposes a deep architecture to automatically color line art videos with the same color style as the given reference images. Our framework consists of a color transform network and a temporal refinement network based on 3U-net. The color transform network takes the target line art images as well as the line art and color images of the reference images as input and generates corresponding target color images. To cope with the large differences between each target line art image and the reference color images, we propose a distance attention layer that utilizes non-local similarity matching to determine the region correspondences between the target image and the reference images and transforms the local color information from the references to the target. To ensure global color style consistency, we further incorporate Adaptive Instance Normalization (AdaIN) with the transformation parameters obtained from a multiple-layer AdaIN that describes the global color style of the references extracted by an embedder network. The temporal refinement network learns spatiotemporal features through 3D convolutions to ensure the temporal color consistency of the results. Our model can achieve even better coloring results by fine-tuning the parameters with only a small number of samples when dealing with an animation of a new style. To evaluate our method, we build a line art coloring dataset. Experiments show that our method achieves the best performance on line art video coloring compared to the current state-of-the-art methods.

P‐142: Late‐News Poster: Research on a Gamut Mapping Color Enhancement Algorithm Based on User Experience

Four gamut mapping algorithms with different color style are designed and implemented in the DCI‐P3 OLED. It is found that the most popular tuning direction was "dark+pure", while "bright+gray" was the least. The keywords "comfortable", "real", "brilliant" and "lifelike" have a high explanatory power to user preferences..

Object detection-oriented style transfer network for panchromatic remote sensing image

A panchromatic image is a remote sensing image that is imaged at the entire visible light band. It often has the highest spatial resolution and is widely used in the fields of resource detection, urban planning, geographic information systems, military, and national defense etc. However, the feature of single-band imaging determines that panchromatic images are usually displayed in the form of grayscale and result in some detailed differences between distinct types of ground objects that are indecipherable. It causes many difficulties for object detection applications. To address this problem, an object detection-oriented style transfer network for panchromatic remote sensing image is designed. In full consideration of the actual requirements of object detection tasks for panchromatic remote sensing image, a style transfer network based on feature fusion is designed, where a style transfer model is trained to transfer the grayscale panchromatic image into the corresponding color style. Further data preprocessing and postprocessing operations are designed to improve the quality of the transferred images and thus prevent negative transfer. DOTA dataset is used to verify the performance of the proposed algorithm. Results show that after the style transfer, the accuracy of object detection on the panchromatic remote sensing images has been significantly improved.

اسلامی ثقافت: ڈاکٹر نصیر احمد ناصر کے افکار کا خصوصی مطالعہ

Culture is the foundation of human life and the identity of any nation or society. Nations that advance in the cultural field experience progress and success. Today's era is actually a time of cultural clash and competition, so it is necessary that we get acquainted with this culture. Will point to civilization and culture, which means the customs and manners that dictate our lives and yours. Civilization and culture mean our faith and beliefs and all the beliefs and ideas that are involved in our individual and social lives. Islamic culture is a standard culture that has the highest values and standards for a society and for any group of human beings. It can put any society on the path of progress and prosperity by making it proud and dignified. The Muslim Ummah is made up of different nations, races, and schools of thought. The oldest civilizations and vast cultures flourished in the same areas where Muslims live today. This diversity, this colorful style, and this ownership of the sensitive areas of the planet are the main characteristics of the Muslim Ummah. The common heritage of history and culture can further increase the strength of this Ummah. In this article, Dr. Naseer Ahmad Nasir's views on Islamic culture are described. Dr. Sahib has described the source of Islamic culture as the Quran and Sunnah. The aesthetic color of Islamic culture has been described.

The Value of Social Media Data in Fashion Forecasting

Problem definition: How to use social media to predict style color and jeans fit sales for a retailer. Academic/practical relevance: Neither retail practice nor the academic literature provides a method for using social media to predict style color and jeans fit sales for a retailer. We present and validate a systematic approach for doing that. Methodology: Demand forecasting in the fashion industry is challenging due to short product lifetimes, long manufacturing lead times, and constant innovation of fashion products. We investigate the value of social media information for color trends and jeans fit forecasting. We partner with three multinational retailers, two apparel and one footwear, and combine their proprietary data sets with web-crawled publicly available data on Twitter and the Google Search Volume Index. We implement a variety of machine learning models to develop forecasts that can be used in setting the initial shipment quantity for an item, arguably the most important decision for fashion retailers. Results: Our findings show that fine-grained social media information has significant predictive power in forecasting color and fit demands months in advance of the sales season, and therefore greatly helps in making the initial shipment quantity decision. The predictive power of including social media features, measured by the improvement of the out-of-sample mean absolute deviation over current practice ranges from 24% to 57%. Managerial implications: To our knowledge, this study is the first to explore and demonstrate the value of social media information in fashion demand forecasting in a way that is practical and operable for fashion retailers. With consistent results across all three retailers, we demonstrate the robustness of our findings over market and geographic heterogeneity, and different forecast horizons. Moreover, we discuss potential mechanisms that might be driving this significant predictive power. Our results suggest that changes in fashion demand are driven more by “bottom-up” changes in consumer preferences than by “top-down” influence from the fashion industry. Funding: This work was supported by Wharton School Fishman-Davidson Center for Service and Operations Management, the Wharton School Baker Retailing Center, and the Wharton School Risk Management Center Russell Ackoff Doctoral Student Fellowship. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1193 .

The Comparative Research on the Color Application of Chinese and Western Film Directors

As one of the common elements of films, color has always played its unique value. It not only decorates the pictures of the films but also assists in expressing the emotions and states of the characters. The extensive use of color in films reflects the self-innovation and technological breakthroughs of modern film and television art. From early black and white films to modern color films, the aesthetic trend of human beings is also evolving with the times. Chinese and Western directors have also made certain differences in the use of color due to subjective and objective factors. This paper takes the different color use styles of Chinese and Western film directors as the research object and adopts the comparative analysis method. Firstly, it briefly describes the development of color films, color functions, and several characteristics in films, and then introduces the different concepts of color used by Chinese and Western directors through specific examples and analyzes the reasons in detail. Finally, the differences in color consciousness and use style between the two are obtained. Chinese directors pay more attention to the symbolic expressiveness of color and are experts in using the freehand style of color, while Western directors attach significance to the structure of color in films, and the color use style is more realistic.