White Balance Research Articles

Hardware-Aware Color-Distorted Image Classifier for Intelligent White Balance

Automatic white balance (AWB) is an important module for cameras and classification of the color-distorted image is critical to realize intelligent AWB. Though accurate classifiers usually can be achieved via deep neural network models, they cannot fit into embedded hardware due to their complexity. To increase the classification accuracy and decrease latency, a lightweight convolutional neural network (CNN) with a histogram layer for AWB (AWBHNet) is constructed, which consists of one histogram layer, one regular convolutional layer, three depth separable convolutional layers, four pooling layers, two fully connected layers and two dropout layers. One-tenth of ImageNet is utilized as the normal image dataset. To generate various distorted colors, histogram shifting and matching are proposed to randomly adjust the histogram position or shape. Furthermore, the extent of shifting or matching is randomly generated to ensure the diversity of color distortion. Subsequently, the proposed AWBHNet and other CNNs are successively trained. Experiments show that the accuracy of the classifier trained by AWBHNet is 0.9150, which is at least 1.33% higher than each regular or lightweight network. Finally, intelligent AWB is realized on smartphones, the inference latency of AWBHNet is 47.6% lower than the best existing network.

Diagnosing anaemia via smartphone colorimetry of the eye in a population of pregnant women.

Objective 
Screening for disease using a smartphone camera is an emerging tool for conditions such as jaundice and anaemia, which are associated with a colour change (yellowing in jaundice; pallor in anaemia) of the external tissues. Based on this, we aimed to test a technique to non-invasively screen for anaemia in a population highly affected by anaemia: pregnant women in India. In this group, anaemia can have severe health consequences for both the mother and child.

Approach
Over 3 years of data collection, in 486 pregnant women in India, we attempted to replicate a previously successful smartphone imaging technique to screen for anaemia. Using smartphone images of the eye and eyelid, we compared two techniques (white balancing and ambient subtraction) to control for variation in ambient lighting, and then extracted 'redness' features from images, which we used as features to predict anaemia via statistical modelling.

Main Results
We found that we were not able to predict anaemia with enough accuracy to be clinically useful, at 89.6% sensitivity and 26.1% specificity. We consider the hypothesis that this may be due to pigmentation on the sclera and palpebral conjunctiva. Visual judgement showed that pigmentation on the sclera, which may affect the measured colour, is more prevalent in pregnant women in India than in preschool aged children in Ghana (a population previously studied in this context). When participants with subjectively judged visible scleral pigmentation are removed, ability to screen for anaemia using the smartphone images slightly improves (93.1% sensitivity, 28.6% specificity).

Significance
These findings provide evidence to reinforce that applying smartphone imaging techniques to understudied populations in the real world requires caution - a promising result in one group may not necessarily transfer to another demographic.

UTILIZATION OF THE IMAGEJ SOFTWARE CAPABILITIES IN DENTISTRY: A LITERATURE REVIEW

Introduction. The method of digital photometry is rapidly gaining widespread use when it comes to quantitative analysis of the color and structure of soft tissues, which is of particular importance in modern dental practice and research. Image J is one of the most widely used digital photometry tools, especially in medical and dental research. As an open source software developed by the US National Institutes of Health, it provides a wide range of image processing and analysis capabilities, including area, color intensity, texture, and the ability to apply various filters and settings. This allows researchers to analyze tissues in detail, track the dynamics of changes, and detect anomalies that are invisible to the naked eye. The purpose of the literature review is to study the potential of the Image J working tools for paraclinical research in the field of prosthetic dentistry. Methodology. A total of 21 literary sources were analyzed, of which 2 were domestic and 19 were foreign. The functionality of the application was studied on the basis of the software “Image J” 1.54g, by National Institutes of Health; USA. Results and discussion. One of the most important functions of the ImageJ program is the measurement of color indicators within the “Adjust” system, which allows for an objective determination of the color characteristics of enamel, dentin, and the oral mucosa. The “Adjust” function provides researchers with a comprehensive set of tools for modifying image parameters, enabling accurate and reliable analysis of both hard dental tissues and soft tissues of the oral cavity. Key tools within this function include brightness and contrast adjustment, threshold segmentation, and color balance, all of which are critical for high-quality dental tissue photometry. It is worth noting that existing literature does not provide information regarding the application of ImageJ functionality in the field of prosthetic dentistry, particularly for analyzing the surfaces of orthopedic structures. This gap highlights the need for further comprehensive research, which could significantly enhance the diagnostic capabilities for evaluating the condition of prosthetic beds and the surfaces of dental prostheses.

Dual-statistic white balance guided 3DLUT for image enhancement

Dual-statistic white balance guided 3DLUT for image enhancement

Color Rendering Correctness Estimation of Aerial and Satellite Images and Restoration of Color Balance

Abstract. Despite the fact that with a certain degree of generalization our idea of object’s color around us can be considered the same, the problem of estimation of the color rendering correctness is very relevant for image’s radiometric correction. The color rendering correctness is one of the most important indicators of aerial and space images visual (photographic) quality, since it determines the quality of objects brightness features transmission by the image. In aerial and space imaging for phototopographic surveying (Bianco et al, 2015), to date, neither an indicator of color imbalance nor a method for its estimation has been developed, therefore it is not possible to assess objectively the presence and degree of color imbalance. Amethod for quantitative estimating the color imbalance of an image suggested in paper. The criterion for evaluating the image’s color imbalance, a method for it’s determining and experimental verification of the developed method by correcting the image’s color balance are shown.

Reading Dye-Based Colorimetric Inks: Achieving Color Consistency Using Color QR Codes

Color consistency when reading colorimetric sensors is a key factor for this technology. Here, we demonstrate how the usage of machine-readable patterns, like QR codes, can be used to solve the problem. We present our approach of using back-compatible color QR codes as colorimetric sensors, which are common QR codes that also embed a set of hundreds of color references as well as colorimetric indicators. The method allows locating the colorimetric sensor within the captured scene and to perform automated color correction to ensure color consistency regardless of the hardware used. To demonstrate it, a CO2-sensitive colorimetric indicator was printed on top of a paper-based substrate using screen printing. This indicator was formulated for Modified Atmosphere Packaging (MAP) applications. To verify the method, the sensors were exposed to several environmental conditions (both in gas composition and light conditions). And, images were captured with an 8M pixel digital camera sensor, similar to those used in smartphones. Our results show that the sensors have a relative error of 9% when exposed with a CO2 concentration of 20%. This is a good result for low-cost disposable sensors that are not intended for permanent use. However, as soon as light conditions change (2500–6500 K), this error increases up to ϵ20 = 440% (rel. error at 20% CO2 concentration) rendering the sensors unusable. Within this work, we demonstrate that our color QR codes can reduce the relative error to ϵ20 = 14%. Furthermore, we show that the most common color correction, white balance, is not sufficient to address the color consistency issue, resulting in a relative error of ϵ20 = 90%.

Enhancing low-illumination imagery using a Deep Convolutional Generative Adversarial Network with weight regularization (DCGAN-WR) and Zero-Reference Deep Curve Estimation (DCE)

Improving low-light images to enhance prediction in various applications has a greater advantage. A two-pronged approach that employs Deep Convolutional Generative Adversarial Networks with weight regularization (DCGAN-WR) and Zero-Reference Deep Curve Estimation (DCE) was used. The model was trained using the LOL dataset, and the results showed significant improvements in image quality. The DCGAN is fine-tuned with Group Lasso regularization to enhance the performance. The DCGAN-WR model is shown to enhance images realistically, demonstrating its capacity to learn characteristics and texture representations from low-light input. Empirical and simulated image comparisons demonstrate remarkable performance under demanding low-light settings. Moreover, the DCE model employs a novel approach that considers color constancy loss, light smoothness, and spatial consistency. Information about the learning dynamics and curve parameter changing capabilities of the model can be visualized by loss function graphs, which aim to maximize picture quality. Compared to the original images, Images generated by the DCE models maintain color accuracy, increase exposure levels, and preserve spatial coherence. A solution for low-illumination image enhancement is achieved through the proposed model DCGAN-WR and DCE. Genuine details are recorded by the GAN model, while the DCE adjusts the exposure levels and color balance to produce improved, aesthetically pleasing, and contextually accurate images. The proposed approach not only outperforms the other methods on the LOL dataset but also exhibits potential for practical use in computer vision tasks, which require higher image quality for precise analysis and interpretation.

A Comparative Study of Physicochemical, Aroma, and Color Profiles Affecting the Sensory Properties of Grape Juice from Four Chinese Vitis vinifera × Vitis labrusca and Vitis vinifera Grapes.

In order to compare the grape juice sensory properties of four common seedless grape varieties (Kyoho, Summer Black, Moldovan, and Sweet Sapphire) in China, a thorough comparison of these grape varieties was conducted. Physicochemical indicators, aroma, and color characteristics were analyzed and compared to a commercially available not-from-concentrate Concord grape juice. The contents of fructose, glucose, and seven organic acids were evaluated. Kyoho and Sweet Sapphire possessed optimal Brix-acid ratio in the range around 35-40. In terms of aroma, 60 volatiles were identified by GC-MS, including 16 alcohols, 9 terpenes, 6 aldehydes, and 4 ketones. Kyoho demonstrated the highest aroma intensity with superior floral and fruity notes, while Sweet Sapphire showed the lowest aroma intensity with a grassy scent. Additionally, grape pigment profiles were investigated by HPLC-PDA-MS. Summer Black grapes featured a vibrant color, and 52.5% of their anthocyanins were acylated, which helps provide good stability in follow-up processing. Concord juice showed the best overall properties, with the most saturated color and delightful aroma. It was suggested that blending Summer Black with Kyoho could be a promising way to achieve balanced color, taste, and aroma of grape juice. This study provides a feasible and promising combination of Chinese local grapes for making all-round high-quality juice products.

Colorful Diffuse Intrinsic Image Decomposition in the Wild

Intrinsic image decomposition aims to separate the surface reflectance and the effects from the illumination given a single photograph. Due to the complexity of the problem, most prior works assume a single-color illumination and a Lambertian world, which limits their use in illumination-aware image editing applications. In this work, we separate an input image into its diffuse albedo, colorful diffuse shading, and specular residual components. We arrive at our result by gradually removing first the single-color illumination and then the Lambertian-world assumptions. We show that by dividing the problem into easier sub-problems, in-the-wild colorful diffuse shading estimation can be achieved despite the limited ground-truth datasets. Our extended intrinsic model enables illumination-aware analysis of photographs and can be used for image editing applications such as specularity removal and per-pixel white balancing.

Nighttime visible and infrared image fusion based on adversarial learning

The task of infrared–visible image fusion (IVIF) aims to integrate multi-modal complementary information and facilitate other downstream tasks, especially under some harsh circumstances. To tackle the challenges of preserving significant information and enhancing visual effects under nighttime conditions, we propose a novel IVIF method based on adversarial learning, namely AdvFusion. It consists of an autoencoder-based generator and a dual discriminator. In particular, the multi-scale features of source images are firstly extracted by ResNet, and then aggregated based on the attention mechanisms and nest connection strategy to generate the fused images. Meanwhile, a global and local dual discriminator structure is designed to minimize the distance between the illumination distributions of the reference images and fused images, which achieves contrast enhancement within fused images and helps to uncover hidden cues in darkness. Moreover, a color loss is utilized to maintain color balance of each fused image, while the widely used perceptual loss and gradient loss are employed to maintain content consistency between the source and fused images. Extensive experiments conducted on five datasets demonstrate that our AdvFusion can achieve promising results compared with the state-of-the-art IVIF methods in terms of both visual effects and quantitative metrics. Furthermore, AdvFusion can also boost the performance of semantic segmentation on MSRS dataset and object detection on M3FD dataset.

ENHANCING UNDERWATER IMAGE QUALITY: EVALUATING COMBINATIVE APPROACHES FOR EFFECTIVE IN SEAGRASS BED ECOSYSTEM

The Complex underwater characteristics, challenges for image processing tasks. These images often have poor visibility due to low contrast, light scattering and various types of interference. There is a lack of exploration into the effectiveness of existing underwater image enhancement methods, particularly in the context of seagrass ecosystems, allows for further investigation. This study aims to explore and evaluate the effectiveness of various methods in underwater image enhancement, including Colour Balanced, CLAHE, and Unsharp Masking and their combinations, starting with converting video data from UTS devices into two-dimensional images. Furthermore, the quality of images taken from underwater cameras placed in a complex and wild seagrass meadow environment was improved using the proposed method, and the quality was evaluated by the SSIM value. The results show that the CLAHE method has the highest average SSIM value of 0.898. Meanwhile, the combined Color Balanced-CLAHE method achieved an SSIM value of 0.683 in a separate evaluation. This combination is an innovative approach to address complex underwater image quality problems, providing a more specific and adaptive solution. Overall, the proposed method is able to improve the visual quality of images on aspects such as clarity, color, and visibility of objects in the image

Research on multi-scale fusion image enhancement and improved YOLOv5s lightweight ROV underwater target detection method

Underwater target detection technology is very important for Marine resources detection and underwater environment perception. The problems of low-quality underwater images, a large amount of calculation, and low accuracy of target detection models still need to be solved. Firstly, a new underwater image enhancement algorithm based on multi-scale fusion is proposed. The algorithm combines the improved white balance algorithm and the improved CLAHE (Contrast Limited Adaptive Histogram Equalization) and realizes the multi-scale fusion strategy by introducing different weight maps, aiming to improve the visual effect and quality of underwater images. Secondly, to further improve the accuracy and efficiency of underwater target detection, an improved YOLOv5s lightweight underwater target detection algorithm is proposed. The algorithm combines the Ghost convolution module, EMA(Efficient Multi-Scale Attention) mechanism, and CARAFE (Content-Aware ReAssembly of FEatures) up-sampling mode. The comparison on the URPC(Underwater Robot Perception Challenge) public dataset shows that the proposed algorithm has achieved significant improvement in terms of model size, parameter amount, calculation amount, and mAP@0.5. Thirdly, to verify the effectiveness of the image enhancement algorithm and underwater target detection algorithm proposed in this paper, the ROV(Remote Operated Vehicle) is used to conduct experiments in the experimental pool. By designing underwater target detection experiments before and after image enhancement, the results show that the enhanced image significantly improves the detection ability of underwater targets. Finally, underwater target detection software is developed to provide strong support for the application of underwater-related fields.

Imaging processing settings of bright field staining images

Abstract. Background: Clear images are vital to studying the cells and exploring the relationships between different parts of cells. Thus, histology is important to view the microscopic structures of cells by using different colors to show. However, the pipeline of getting a better and more useful image of bright field staining images is still a challenge. This article introduces different processed images to get a good condition to improve image quality. Methods: Four staining ways, including Periodic acidSchiff (PAS) staining, Hematoxylin and Eosin (H&E) staining, Trichrome Staining, and immunohistochemistry (IHC), have been used on the liver, heart, and brain sections. Photoshop and Image J were used to process images of different conditions. The software settings have been compared. Results: 60X Oil immersion can provide nice details for the liver, heart, and brain with PAS, Trichrome, and H&E staining. By comparing different settings on software, the following settings can be optimal options: 300 PPI resolution for optimal size and clarity, gamma to adjust the light and dark, color balance to make the colors more comfortable to the eye, TIFF format to meet most requirements. Image J, compared to Photoshop, can solve many image processing and analysis problems with a smaller installation package and is free to download. Conclusions: By using optimized settings of image software, researchers can get better pictures and assist in solving more scientific problems.

Advanced Image Preprocessing and Integrated Modeling for UAV Plant Image Classification

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for resolution enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, and white balance adjustments for accurate color representation. These preprocessing steps ensure high-quality input data, leading to better model performance. For feature extraction and classification, we employ a pre-trained VGG-16 deep convolutional neural network, followed by machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost). This hybrid approach, combining deep learning for feature extraction with machine learning for classification, not only enhances classification accuracy but also reduces computational resource requirements compared to relying solely on deep learning models. Notably, the VGG-16 + SVM model achieved an outstanding accuracy of 97.88% on a dataset preprocessed with ESRGAN and white balance adjustments, with a precision of 97.9%, a recall of 97.8%, and an F1 score of 0.978. Through a comprehensive comparative study, we demonstrate that the proposed framework, utilizing VGG-16 for feature extraction, SVM for classification, and preprocessed images with ESRGAN and white balance adjustments, achieves superior performance in plant species identification from UAV imagery.

Argon Ion Implantation as a Method of Modifying the Surface Properties of Wood-Plastic Composites.

Wood-plastic composites (WPCs) combine the properties of plastics and lignocellulosic fillers. A particular limitation in their use is usually a hydrophobic, poorly wettable surface. The surface properties of materials can be modified using ion implantation. The research involved using composites based on polyethylene (PE) filled with sawdust or bark (40%, 50%, and 60%). Their surfaces were modified by argon ion implantation in three fluencies (1 × 1015, 1 × 1016, and 1 × 1017 cm-2) at an accelerating voltage of 60 kV. Changes in the wettability, surface energy, and surface colour of the WPCs were analysed. It was shown that argon ion implantation affects the distinct colour change in the WPC surface. The nature of the colour changes depends on the filler used. Implantation also affects the colour balance between the individual variants. Implantation of the WPC surface with argon ions resulted in a decrease in the wetting angle. In most of the variants tested, the most significant effect on the wetting angle changes was the ion fluence of 1 × 1017 cm-2. Implantation of the WPC surface also increased the surface free energy of the composites. The highest surface free energy values were also recorded for the argon ion fluence of 1 × 1017 cm-2.

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 portable dual-mode colorimetric and fluorescence sensing platform for RGB detection in liquid solutions

The color of liquids is crucial in food safety, pharmaceuticals, cosmetics, and environmental monitoring. Traditional color measurement methods typically require expensive spectrophotometers, limiting their utility in rapid testing and cost-effective applications. This study developed a cost-effective, portable dual-mode colorimetric and fluorescence sensing platform with using the TCS230 sensor and STM32F103ZET6 microcontroller for real-time RGB color detection. Equipped with built-in white and ultraviolet light sources, the platform operates reliably across different environmental conditions, aided by its darkroom design and white balance calibration strategy to minimize ambient light interference. Tests with fluorescent and dye solutions show stability and reliability of the platform. The relationship between color saturation and concentration under different concentrations showed the excellent correlation coefficients above 0.99 for CuSO4 and KMnO4 solutions. The measurement deviations of 1.93% to 3.41%, render it an efficient tool for on-site and multiple-mode color testing.

Effect of Litsea cubeba and Cinnamon Essential Oil Nanoemulsion Coatings on the Preservation of Plant-Based Meat Analogs.

Plant-based meat analogs (PBMAs) are promising sustainable food sources. However, their high moisture and protein contents make them prone to microbial deterioration, limiting their shelf life and sensory appeal. This study explored enhancing PBMAs' shelf life using nanoemulsions of Litsea cubeba and cinnamon essential oils, emulsified with chitosan and Tween 80. The composite nanoemulsion, produced through high-pressure homogenization, exhibited a droplet size of 4.99 ± 0.03 nm, a polydispersity index (PDI) of 0.221 ± 0.008, and a zeta potential of 95.13 ± 2.67 mV, indicating remarkable stability (p < 0.05). Applied to PBMAs stored at 4 °C, it significantly improved color and pH balance and reduced thiobarbituric acid reactive substances and cooking loss. Most notably, it inhibited the growth of Escherichia coli and Staphylococcus aureus, curbing spoilage and protein oxidation, thereby extending the products' shelf life and preserving sensory quality. As shown above, the encapsulation of LCEO/CEO in nanoemulsions effectively inhibits spoilage and deterioration in PBMAs, improving flavor and quality more than direct addition. Future studies should explore using various essential oils and emulsifiers, as well as alternative encapsulation techniques like microcapsules and nanoparticles, to further prevent PBMA deterioration.

A solution for the automatic detection of expansion joints in dam stilling pools using underwater robots

This research addresses the critical challenges of detecting and measuring underwater concrete structure expansion joints (CSEJ), with a particular focus on dam stilling basin environments. A full set of algorithms for detecting expansion joints with underwater robots is presented by the research, enabling real-time and offline analysis along with visualization. These algorithms introduce the use of Convolutional Neural Networks (CNNs) for image feature extraction and segmentation in CSEJ detection, significantly improving accuracy and adaptability over traditional methods. An automated approach known as the Underwater Dam Crack Detection Network (UDCD Network) was introduced by this research. And advanced attention gates and hybrid dilated convolutions to enhance its detection capabilities are incorporated in this system. Additionally, a three-phase deep transfer learning strategy during training was utilized in this research, which significantly improves the model's performance. The research suggests an underwater image restoration algorithm that uses a dark channel prior and dynamic white balance correction to improve image clarity. Furthermore, unique underwater environmental challenges were tackled by this research, such as non-contact measurement of expansion joint widths using a view-geometry-based algorithm and comprehensive detection of dam wall surfaces. Experimental validation demonstrates that the advanced imaging technologies of the proposed underwater robots hold significant potential for autonomous detection of expansion joints in dam stilling basins.

An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring.

Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people's exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.