Image Color Research Articles

Accessibility of COVID-19-related Public Websites in Japan

ABSTRACT Background During the COVID-19 pandemic, accessible websites and online content have become more critical worldwide as patients seek information about their medical concerns with limited access to care. However, there are still research gaps regarding the accessibility to COVID-19 information in Japan. Purpose This study systematically and quantitatively analyzed COVID-19-related local government websites from official COVID-19 for compliance with WCAG guidelines. Methods On June 25, 2022, we analyzed COVID-19 web portals by Japanese local governments using AChecker, WAVE, and miChecker. These tools are automated accessibility evaluation tool based on Web Content Accessibility Guidelines (WCAG), which are widely used international standards in accessibility. Results Of the 72 websites included, the average number of errors per page was 2.7 (range 0–38) for WAVE and 8.7 (range 0–104) for WCAG 2.0 AA, respectively. The number of errors was lower than the number of accessibility errors on general websites. “Perceivable” errors, such as ambiguous colors/contrasts and missing alternative text, were prominently displayed at the top of the list of web page accessibility errors. Discussion The accessibility of the COVID-19 website of the Japanese local government was deemed satisfactory compared with the average number of accessibility errors on the top 1,000,000 home pages worldwide (50.8 per page). However, the websites lacked support in images (color and text) and audio guidance for users with visual and cognitive impairments. Translation to Health Education Practice For proper user perception, COVID-19 information had to be presented with clear contrast, alternative text, and informative captions.

Research on High Performance Apple Recognition Model Construction Strategy Based on Deep Learning

In order to improve the working efficiency and environmental adaptability of apple picking robots, which can realize accurate recognition and localization, ripening assessment, and quality assessment under complex situations such as "leaf occlusion", "branch occlusion", "fruit occlusion", "mixed occlusion", etc., this paper proposes a Faster R-CNN-based apple recognition method. "mixed occlusion" and other complex situations to achieve accurate recognition and localization, maturity assessment, quality assessment, the paper proposes an apple recognition method based on Faster R-CNN. The method combines the cv2.inRange and cv2.fitEllipse functions in OpenCV to optimize the model, and performs color segmentation and shape fitting by setting up color and shape thresholds to recognize apples. The trained model was tested to have a recall of 88.44% under the validation set, an accuracy of 90.35%, an F1 value of 89.38%, and a recognition time of about 0.3 s per image.The experimental results showed that the number of apples in the dataset showed a normal distribution, with the number distributed more in the range of 0 to 10. Candidate frames are screened by RPN, and apple coordinates are derived based on the coordinate transformation formula, and the experimental results show that the apples are more densely distributed near the range of x=100, y=100. The image color distribution was extracted through color histogram, and the entropy value of GLCM, contrast as the texture feature of apples, and the classification prediction of apple ripeness was carried out by using Random Forest Model, and the accuracy of the model prediction was 83.2%, the precision rate was 84.1%, and the recall rate was 83.5%, and the experimental results showed that 88.7% of the apples were ripe. Image edges were extracted by the Canny edge detection algorithm and the area values of pixel points within the edges were calculated. The ratio of pixel area converted to apple mass was estimated to be 0.007 based on the image resolution and apple density, and the test results indicated that the mass of apples was mostly concentrated in the range of 0~100g.

Image inpainting based on GAN-driven structure- and texture-aware learning with application to object removal

In this paper, a novel deep learning-based image inpainting framework consisting of restoring image structure and reconstructing image details from corrupted images is proposed. Most image inpainting methods in the literature aim at restoring image details, outlines, and colors, simultaneously, which may suffer from blurring, deformation, and unreasonable content recovery due to interference among various information. To solve these problems, a two-stage image inpainting deep neural network based on GAN (generative adversarial network) architecture is proposed. The proposed inpainting framework consists of two modules: (1) the first stage, called the structure-aware learning stage, aims at learning a GAN-based structure restoration network, focusing on recovering the low-frequency image component, including colors and outlines of the missing regions of the input corrupted image; and (2) the second stage, called the texture-aware learning stage, aims at learning a GAN-based detail refinement network, focusing on rebuilding the high-frequency image details and texture information. In particular, we also propose to remove details from the training images to better train the structure restoration network to avoid inadequate image structure recovery induced by richer image textures, where the detail reconstruction task is left to the second stage. This strategy achieves to balance the workload between the two stages and the image quality can be progressively enhanced through the two stages. Experimental results have shown that the proposed deep inpainting framework quantitatively and qualitatively achieves state-of-the-art performance on the well-known datasets, including the CelebA, Places2, and ImageNet datasets, compared with existing deep learning-based image inpainting approaches. More specifically, in terms of the two well-known image quality assessment metrics, PSNR (peak signal-to-noise ratio) and SSIM (structural similarity), the improvement percentage of the proposed method, compared with the baseline approach, respectively, ranges from 3.23 % to 11.12 %, and 1.95 % to 13.39 %. The improvements have been shown to stably and significantly outperform the compared state-of-the-art methods in most types of inpainting mask. We also show that the proposed method is applicable to image editing in object removal from a single image.

Quality Inspection and Problem Analysis of Satellite Image Data in Land Use Survey

Abstract. In order to improve the data quality of land use remote sensing monitoring images, this article introduces the process of generating satellite image data, elaborates on the content of satellite image data verification in land use remote sensing monitoring, and proposes quality issues and improvement measures for satellite image data. Taking the discovered satellite image data quality issues as an example, compared with quality inspection standards, it was found that the main problems in the results were projection parameter errors, image color distortion, image blurring, and position accuracy exceeding limits. It is recommended to check the above issues during the image production stage, analyze the reasons for exceeding the position accuracy limit, image distortion, and embossing, and provide relevant suggestions. Provided strong technical support for land use surveys.

Application of gaussian filter and extraction features for quality control of fruit raw materials in the puree industry

The purpose of research in using Android-based raw material quality control applications in fruit puree factories is to enhance the industry's precision, uniformity, and efficiency. This application is developed to implement digital image processing utilizing two distinct methods: Gaussian filters and feature extraction. The implemented application captures guava data using the camera of an Android phone and subsequently resizes the image to dimensions of 600x600 pixels. Subsequently, the image colors are recovered by employing a Gaussian filter that operates on normalized Red-Green-Blue values. A minimum of 120 photos of red guava fruit from the raw material of one of the puree companies were subjected to image processing procedures. The application solely focuses on the color and texture of the fruit's skin, ensuring that the sample remains undamaged while adhering to hygienic guidelines. The ripeness degree of guava fruit is determined by employing an image classification algorithm with the K-nearest neighbor method. The application validation using K-fold cross-validation achieved an accuracy of 90.0% and a precision of 90.27% when applied to color imagery. When feature extraction was used, the accuracy was 83.3%, with a precision of 83.4%. Color extraction provides a more precise method for identifying ripe guava. The utilization of guava fruit ripeness detection in the quality control of raw materials for the puree sector has been simplified and made more user-friendly through the development of an Android-based application. Officers are not obligated to possess specialized expertise regarding the quality of raw materials.

An unsupervised multi-focus image fusion method via dual-channel convolutional network and discriminator

The challenge in multi-focus image fusion tasks lies in accurately preserving the complementary information from the source images in the fused image. However, existing datasets often lack ground truth images, making it difficult for some full-reference loss functions (such as SSIM) to effectively participate in model training, thereby further affecting the performance of retaining source image details. To address this issue, this paper proposes an unsupervised dual-channel dense convolutional method, DCD, for multi-focus image fusion. DCD designs Patch processing blocks specifically for the fusion task, which segment the source image pairs into equally sized patches and evaluate their information to obtain a reconstructed image and a set of adaptive weight coefficients. The reconstructed image is used as the reference image, enabling unsupervised methods to utilize full-reference loss functions in training and overcoming the challenge of lacking labeled data in the training set. Furthermore, considering that the human visual system (HVS) is more sensitive to brightness than color, DCD trains the dual-channel network using both RGB images and their luminance components. This allows the network to focus more on the brightness information while preserving the color and gradient details of the source images, resulting in fused images that are more compatible with the HVS. The adaptive weight coefficients obtained through the Patch processing blocks are also used to determine the degree of preservation of the brightness information in the source images. Finally, comparative experiments on different datasets also demonstrate the superior performance of DCD in terms of fused image quality compared to other methods.

Content-based fabric recommendation system integrating image and text information

The increasing popularity of e-commerce and online fashion platforms has led to a growing demand for effective fabric recommendation systems. Choosing the suitable fabric is crucial to achieving desired aesthetic outcomes, ensuring product quality, and enhancing user satisfaction in the fashion design and textile manufacturing industries. Although our research group has made significant progress in fabric retrieval, we have encountered several challenges in practical collaborations with industry partners. These challenges include users’ difficulty expressing their preferences and the lack of personalization in fabric retrieval. We proposed a content-based fabric recommendation system that combines image and text information to address these challenges. Our approach employed various methods to extract hand-crafted features, such as image color and texture, to characterize the visual communication of fabrics. We combined this with text information to provide a more comprehensive fabric representation. By mining user preferences and employing personalized recommendation algorithms, our system delivered personalized fabric recommendations to cater to individual needs. Experimentation with comparison texts, colors, and textures separately or together. According to the assessment results, the highest F1 value of 83% can be achieved when the color, texture, and text combine with the color: texture: text = 1:1:1 and the filter value = 2.2. Precision and recall rates are also 83% at this point. Our proposed fabric recommendation system is useful and efficient.

패션 이미지 데이터를 활용한 딥러닝 기반의 의류속성 분류

Attributes such as material, color, and fit in fashion images are important factors for consumers to purchase clothing. However, the process of classifying clothing attributes requires a large amount of manpower and is inconsistent because it relies on the subjective judgment of human operators. To alleviate this problem, there is a need for research that utilizes artificial intelligence to classify clothing attributes in fashion images. Previous studies have mainly focused on classifying clothing attributes for either tops or bottoms, so there is a limitation that the attributes of both tops and bottoms cannot be identified simultaneously in the case of full-body fashion images. In this study, we propose a deep learning model that can distinguish between tops and bottoms in fashion images and classify the category of each item and the attributes of the clothing material. The deep learning models ResNet and EfficientNet were used in this study, and the dataset used for training was 1,002,718 fashion images and 125 labels including clothing categories and material properties. Based on the weighted F1-Score, ResNet is 0.800 and EfficientNet is 0.781, with ResNet showing better performance.

Smartphone-based colorimetric detection platform using color correction algorithms to reduce external interference

Smartphone-based digital image colorimetry is a powerful, fast, low-cost approach to detecting target analytes. However, lighting conditions and camera parameters easily affect the detection results, significantly curtailing its applicability in multiple scenarios. In this study, an Android-based mobile application (SMP-CC) is developed, which offers a comprehensive package that includes image acquisition, color correction, and colorimetric analysis functions. Using a custom color card, a built-in algorithm in SMP-CC can minimize the color difference between the standard color block image captured by different smartphones under different lighting conditions and the standard value by an LS171 colorimeter less than 4.36. The algorithm significantly eliminates the impacts of external lighting conditions and differences in cell phone models. Furthermore, the feasibility of SMP-CC was verified by successful colorimetric detection of urine pH, glucose, and protein, demonstrating its potential in smartphone-based digital image colorimetry.

Image recognition technology provides insights into relationships between anthocyanin degradation and color variation during jet drying of black carrot

To identify interesting relationships between anthocyanin degradation and color variation during food processing, black carrot slice (BCS) was dried by air-impingement jet drying (AIJD) and hot air drying (HAD). AIJD was a better technology for drying BCS than HAD. Results of colorimeter determination showed that the color of BCS was significantly changed during AIJD at 50, 60 and 70 °C. UHPLC-QqQ-MS/MS analysis found that AIJD-induced degradations of main BCS anthocyanins, cyanidin-3-xylosyl(feruloylglucosyl)galactoside and cyanidin-3-xylosyl(sinapoylglucosyl)galactoside, belonged to non-spontaneous endothermic reactions, which followed the 0.5- and 1-order kinetic equations, respectively. Anthocyanin content and colors obtained from colorimeter presented strong positive correlation, particularly the a* and chroma values. We further developed a Python script based on image recognition technology to visualize the correlation matrixes between the anthocyanin contents and colors of BSC images. The plots revealed that strong positive correlations between anthocyanins and colors primarily concentrated in the sample's periphery following a concentric pattern.

Image-based identification of optical quality and functional properties in inkjet-printed electronics using machine learning

AbstractWe propose a novel image-analysis based machine-learning approach to the fully-automated identification of the optical quality, of functional properties, and of manufacturing parameters in the field of 2D inkjet-printed test structures of conductive traces. To this end, a customizable modular concept to simultaneously identify or predict dissimilar properties of printed functional structures based on images is described and examined. An application domain of the concept in the printing production process is outlined. To examine performance, we develop a dataset of over 5000 test structures containing images and physical characteristics, which are manufactured using commercially available materials. Functional test structures are fabricated via a single-nozzle vector-based inkjet-printing system and thermally sintered. Physical characterization of electrical conductance, image capturing, and evaluation of the optical quality of the test structures is done by an automatic in-house built measurement station. Conceptionally, the design of a convolutional neural network is described to identify the optical quality and physical characteristics based only on acquired images. A mathematical apparatus that allows assessment of the identification accuracy is developed and described. The impact of printing resolution, of emerging defects in the geometry of printed structures, and of image quality and color space on the identification accuracy is analyzed. Quality groups related to the printing resolution that affect identification accuracy are determined. Supplementarily, we introduce not yet reported classification of processes related to the fabrication of printed functional structures, adopted from the process analytical technology.

SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN

Crop disease detection and crop baking stage judgement require large image data to improve accuracy. However, the existing crop disease image datasets have high asymmetry, and the poor baking environment leads to image acquisition difficulties and colour distortion. Therefore, we explore the potential of the self-attention mechanism on crop image datasets and propose an innovative crop image data-enhancement method for recurrent generative adversarial networks (GANs) fused with the self-attention mechanism to significantly enhance the perception and information capture capabilities of recurrent GANs. By introducing the self-attention mechanism module, the cycle-consistent GAN (CycleGAN) is more adept at capturing the internal correlations and dependencies of image data, thus more effectively capturing the critical information among image data. Furthermore, we propose a new enhanced loss function for crop image data to optimise the model performance and meet specific task requirements. We further investigate crop image data enhancement in different contexts to validate the performance and stability of the model. The experimental results show that, the peak signal-to-noise ratio of the SM-CycleGAN for tobacco images and tea leaf disease images are improved by 2.13% and 3.55%, and the structural similarity index measure is improved by 1.16% and 2.48% compared to CycleGAN, respectively.

Image dehazing using non-local haze-lines and multi-exposure fusion

Images captured under haze conditions suffer from color distortion and low saturation due to the light propagates through scattering particles, causing light intensity attenuation and direction deflection, which affects the imaging quality of the visual system. To deal with these issues, we propose an image dehazing method based on non-local haze-line and multi-exposure fusion, called NHMF. Specifically, we first equalize the brightness and color of the input image according to a multi-scale fusion strategy. Meanwhile, we use the dark channel prior based on a local window to solve the atmospheric light value. Afterward, we employ the preprocessed image to obtain fog lines with better generalization performance that enhances the estimation of the transmission rate. Furthermore, we introduce weighted least-squares filtering to refine transmittance estimation accuracy further and ultimately employ an atmospheric scattering model to reverse process the haze-free image. Our extensive experiments on three image enhancement datasets demonstrate the effectiveness of our approach in quantitative and qualitative dehazing of images with haze. Moreover, our method exhibits excellent generalization performance in dehazing remote sensing images and enhancing underwater images.

Development and validation of a nonverbal consensus-based semantic memory paradigm in patients with epilepsy.

Brain areas implicated in semantic memory can be damaged in patients with epilepsy (PWE). However, it is challenging to delineate semantic processing deficits from acoustic, linguistic, and other verbal aspects in current neuropsychological assessments. We developed a new Visual-based Semantic Association Task (ViSAT) to evaluate nonverbal semantic processing in PWE. The ViSAT was adapted from similar predecessors (Pyramids & Palm Trees test, PPT; Camels & Cactus Test, CCT) comprised of 100 unique trials using real-life color pictures that avoid demographic, cultural, and other potential confounds. We obtained performance data from 23 PWE participants and 24 control participants (Control), along with crowdsourced normative data from 54 Amazon Mechanical Turk (Mturk) workers. ViSAT reached a consensus >90% in 91.3% of trials compared to 83.6% in PPT and 82.9% in CCT. A deep learning model demonstrated that visual features of the stimulus images (color, shape; i.e., non-semantic) did not influence top answer choices (p = 0.577). The PWE group had lower accuracy than the Control group (p = 0.019). PWE had longer response times than the Control group in general and this was augmented for the semantic processing (trial answer) stage (both p < 0.001). This study demonstrated performance impairments in PWE that may reflect dysfunction of nonverbal semantic memory circuits, such as seizure onset zones overlapping with key semantic regions (e.g., anterior temporal lobe). The ViSAT paradigm avoids confounds, is repeatable/longitudinal, captures behavioral data, and is open-source, thus we propose it as a strong alternative for clinical and research assessment of nonverbal semantic memory.

Simulation of e-learning visualization technology based on image enhancement in the communication mode of public performance art videos

With the simulation of public performing arts video communication modes, this study aims to explore the application of image-enhanced electronic learning visualization techniques in this field. Through the introduction of this technology, it aims to improve the effect of e-learning and enhance learners' understanding and perception of public performing arts videos. Through the acquisition and processing of public performing arts videos, the image enhancement technology is used to improve the image clarity and color saturation. Based on the e-learning platform, the enhanced video will be transmitted online, so that learners can watch through electronic devices and carry out corresponding learning operations. Finally, the learning effect of traditional learning and image-based e-learning. The results show that e-learning visualization technology based on image enhancement can significantly improve learners' understanding and feelings of public performing arts videos. Through the enhanced video images, learners can more clearly observe the actors' performance movements, facial expressions and other details, so as to better understand the connotation and communication meaning of the public performing arts.

Unsupervised Bidirectional Contrastive Reconstruction and Adaptive Fine-Grained Channel Attention Networks for image dehazing

Recently, Unsupervised algorithms has achieved remarkable performance in image dehazing. However, the CycleGAN framework can lead to confusion in generator learning due to inconsistent data distributions, and the DisentGAN framework lacks effective constraints on generated images, resulting in the loss of image content details and color distortion. Moreover, Squeeze and Excitation channel attention employs only fully connected layers to capture global information, lacking interaction with local information, resulting in inaccurate feature weight allocation for image dehazing. To solve the above problems, in this paper, we propose an Unsupervised Bidirectional Contrastive Reconstruction and Adaptive Fine-Grained Channel Attention Networks (UBRFC-Net). Specifically, an Unsupervised Bidirectional Contrastive Reconstruction Framework (BCRF) is proposed, aiming to establish bidirectional contrastive reconstruction constraints, not only to avoid the generator learning confusion in CycleGAN but also to enhance the constraint capability for clear images and the reconstruction ability of the unsupervised dehazing network. Furthermore, an Adaptive Fine-Grained Channel Attention (FCA) is developed to utilize the correlation matrix to capture the correlation between global and local information at various granularities promotes interaction between them, achieving more efficient feature weight assignment. Experimental results on challenging benchmark datasets demonstrate the superiority of our UBRFC-Net over state-of-the-art unsupervised image dehazing methods. This study successfully introduces an enhanced unsupervised image dehazing approach, addressing limitations of existing methods and achieving superior dehazing results. The source code is available at https://github.com/Lose-Code/UBRFC-Net.

A novel approach for color-balanced reference image selection for breast histology image normalization

Histology image analysis plays a crucial role in pathological research and diagnosis. The effectiveness of Computer-Aided Detection procedures largely depends on the normalization of histology images to ensure precise diagnoses. On the other hand, the success of this normalization process is tied to the choice of an appropriate reference image, which acts as a standard for aligning and modifying the color and intensity of other images in the dataset. However, the lack of a universally reliable method for selecting an appropriate reference image remains a substantial obstacle in histology image analysis. In this direction, the study introduces an automated procedure for selecting a reference image that is designed to accurately represent the entire spectrum of the dataset or its specific class under investigation. The impact of the proposed method on the morphological and structural quality of normalized histology images is investigated using three established normalization techniques: Reinhard, Macenko, and Vahadane. Moreover, the study also utilized two strategies: the global reference image strategy, where a single reference image is chosen for the entire dataset, and the class-specific reference image strategy, which considers the unique characteristics of each class. The results demonstrate that the proposed automatic reference image selection procedure consistently outperforms random selection in both strategies, yielding higher metric scores (SSIM, QSSIM, and PCC). Furthermore, the class-specific reference image approach significantly improves the image quality compared to random selection. The proposed method enhances alignment and accuracy in histology image normalization by leveraging class-specific reference images, facilitating more reliable analysis in histopathology research and clinical applications.

Underwater image sharpening and color correction via dataset based on revised underwater image formation model

Underwater image sharpening and color correction via dataset based on revised underwater image formation model

Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics

Medical imaging AI systems and big data analytics have attracted much attention from researchers of industry and academia. The application of medical imaging AI systems and big data analytics play an important role in the technology of content based remote sensing (CBRS) development. Environmental data, information, and analysis have been produced promptly using remote sensing (RS). The method for creating a useful digital map from an image data set is called image information extraction. Image information extraction depends on target recognition (shape and color). For low-level image attributes like texture, Classifier-based Retrieval(CR) techniques are ineffective since they categorize the input images and only return images from the determined classes of RS. The issues mentioned earlier cannot be handled by the existing expertise based on a keyword/metadata remote sensing data service model. To get over these restrictions, Fuzzy Class Membership-based Image Extraction (FCMIE), a technology developed for Content-Based Remote Sensing (CBRS), is suggested. The compensation fuzzy neural network (CFNN) is used to calculate the category label and fuzzy category membership of the query image. Use a basic and balanced weighted distance metric. Feature information extraction (FIE) enhances remote sensing image processing and autonomous information retrieval of visual content based on time-frequency meaning, such as color, texture and shape attributes of images. Hierarchical nested structure and cyclic similarity measure produce faster queries when searching. The experiment’s findings indicate that applying the proposed model can have favorable outcomes for assessment measures, including Ratio of Coverage, average means precision, recall, and efficiency retrieval that are attained more effectively than the existing CR model. In the areas of feature tracking, climate forecasting, background noise reduction, and simulating nonlinear functional behaviors, CFNN has a wide range of RS applications. The proposed method CFNN-FCMIE achieves a minimum range of 4–5% for all three feature vectors, sample mean and comparison precision-recall ratio, which gives better results than the existing classifier-based retrieval model. This work provides an important reference for medical imaging artificial intelligence system and big data analysis.

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.