White Balance Research Articles

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.

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.

Short communication: White balance for self‐luminous highlights in images

Short communication: White balance for self‐luminous highlights in images

CDAN: Convolutional dense attention-guided network for low-light image enhancement

Low-light images, characterized by inadequate illumination, pose challenges of diminished clarity, muted colors, and reduced details. Low-light image enhancement, an essential task in computer vision, aims to rectify these issues by improving brightness, contrast, and overall perceptual quality, thereby facilitating accurate analysis and interpretation. This paper introduces the Convolutional Dense Attention-guided Network (CDAN), a novel solution for enhancing low-light images. CDAN integrates an autoencoder-based architecture with convolutional and dense blocks, complemented by an attention mechanism and skip connections. This architecture ensures efficient information propagation and feature learning. During training, we utilize a composite loss function that merges L2 and VGG losses for improved numeric and perceptual results. Furthermore, a dedicated post-processing phase refines color balance and contrast. Our approach demonstrates notable progress compared to state-of-the-art results in low-light image enhancement, showcasing its robustness across a wide range of challenging scenarios. Our model performs remarkably on benchmark datasets, effectively mitigating under-exposure and proficiently restoring textures and colors in diverse low-light scenarios. This achievement underscores CDAN's potential for diverse computer vision tasks, notably enabling robust object detection and recognition in challenging low-light conditions.

Self-correcting deep learning for estimating rice leaf nitrogen concentration with mobile phone images

As a pivotal role in crop development, the application of nitrogen fertilizers enhances yields, while excessive use poses environmental risks. Precision nitrogen management requires rapid crop nitrogen assessment in field conditions and mobile phone images can be a promising approach. However, few studies have investigated how arbitrary white balance and exposure value settings affect crop nitrogen diagnosis using phone photos. Therefore, a new deep learning framework: self-correcting leaf nitrogen concentration estimation network (SCLNC-Net), was proposed to explore the potential of mobile phone images for non-destructive nitrogen status monitoring. Imperfect camera metering and incorrect user operations were addressed through attention U-Net for automatic white balance and exposure correction. Then a lightweight deep learning algorithm MobileNetV3 was utilized to estimate rice leaf nitrogen concentration based on the self-corrected images. Compared with most deep learning models, SCLNC-Net has fewer parameters and higher computational efficiency, which is better suited for deployment on mobile platforms. The results revealed that SCLNC-Net enhanced image quality and acquired high accuracy in the estimation of rice leaf nitrogen concentration (R2 = 0.84, RMSE = 0.39 %, RRMSE = 13 %). Moreover, the robustness and effectiveness of the proposed SCLNC-Net remained excellent across various shooting angles, mobile phone types, and rice varieties. This study provides insights into rapid and cost-efficient nutrient assessment and precision nitrogen management.

The reused-disposable scope in flexible ureteroscopy for stones as a cost-conscious approach: Reporting the outcomes of a real-world practice multicenter study of 2183 patients by the team of worldwide endourological researchers group

ABSTRACT Introduction: We aimed to assess complications and stone-free rate of flexible ureteroscopy (FU) reusing disposable scopes (RDS) after repeated sterilization. Methods: Data from adults from 11 centers were retrospectively reviewed (January 2020–December 2022). Inclusion criteria were proximal ureteral/renal stone(s). All cases were performed using an RDS to save costs for patients who come from economically challenged environments. Residual fragments (RFs) were defined as single fragment ≥4 mm or multiple fragments of any size within 3 months. Continuous variables are presented as median and interquartile range. Results: Two thousand one hundred and eighty-three patients were included, of whom 67.0% were male. Median age was 48.0 (36–59) years. The median stone diameter was 10.2 (9–14) mm. Flash sterilization was used in 90.2% (plasma in 60.5%). Approximately, 88% had FU with an RDS used ≤2 times (12%: 3–5 times). RDS needed to be changed intraoperatively in 3.9% of cases due to its malfunction. Commonly, defects in RDS function were reported in upward (1.6%) and downward deflection (6.5%) and image quality on white balancing (4.7%). Fever >38C was seen in 13.7% of cases, and sepsis in 0.5%. RFs were found in 31.4% of cases. Lower pole (odds ratio [OR] 5.63) or pelvis stone (OR 4.67), faulty scopes (OR 12.8), and total operation time (OR 1.05) were factors associated with higher odds of RFs. Stone size (OR 1.09), positive urine culture (OR 1.67), interpolar stone (OR 1.68), and prestenting (OR 1.37) were factors associated with higher odds of fever/sepsis. Conclusions: RDS was used as a cost-conscious approach with a low rate of serious infections but with a high rate of RFs.

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.

Fast Sand-dust Image Enhancement Algorithm by Blue Channel Compensation and Guided Image Filtering

Computer vision systems are significantly impacted by the color variation and low visibility of images taken under sand-dust conditions To address the aforementioned issues, we provide a quick and efficient algorithm to improve photos taken under sand-dust situations. The loss value in the blue channel is first adjusted for. Subsequently, the image deteriorated by sand and dust is color corrected using white balance technology. Lastly, the picture contrast and edge correctness are improved using guided image filtering, and the image detail information is improved by using an adaptive method to determine the detail layer's magnification factor. The approach can successfully restore the fading features of sand-dust-deteriorated photos, according to the experimental results on a sizable number of degraded images by the dust in a brief amount of time, and enhance image sharpness. The suggested method may greatly improve the photos taken during sand-dust weather conditions, as shown by experimental findings via qualitative and quantitative assessments, and the outcomes are superior to those of other ways.

Fluorescence-based determination of tobramycin using a portable smartphone-based device

The study presents the development of a robust portable smartphone-based device for determining tobramycin in drugs and urine by a fluorescence-based method. As existing analytical methods for the determination of tobramycin often require complex equipment and time-consuming procedures, the proposed approach offers simplicity, portability and real-time monitoring capabilities. The spectrofluorimetric method is based on the reaction of tobramycin and fluorescamine in an alkaline medium. The measurement of analytical signals was enabled with a specially designed device integrating a 3D printed adapter, LEDs, a macro lens, an Arduino-based microcontroller, and asmartphone. The traditional spectrofluorimetry was used as a reference method. Preliminary and optimisation studies were conducted to select the most suitable experimental conditions (e.g. reagent concentration and reaction time) as well as instrumental parameters (e.g. selection of the appropriate RGB model component, ISO, and white balance). The performance of the method was validated, and then verified against conventional spectrofluorimetry, showing comparable accuracy and precision. The method implemented in a smartphone-based device showed a linear range of 0.10–1.00 mgL-1 with a detection limit of 0.03 mgL-1. Synthetic samples were tested to verify analytical usability, followed by analysis of pharmaceutical and urine samples. The method showed good precision (CV<3.4 %) and recovery values (92.3–114.1 %), consistent with spectrofluorimetric results (CV<5.9 %, recovery 92.3–119.8 %). Comparison with other methods highlighted the simplicity and efficiency of the developed approach. The research reveals that it is competitive with existing well-established methods. The proposed device offers reliable results with low reagent and sample consumption, making itsuitable for on-the-go analysis in non-laboratory conditions.

An Underwater Crack Detection System Combining New Underwater Image-Processing Technology and an Improved YOLOv9 Network.

Underwater cracks are difficult to detect and observe, posing a major challenge to crack detection. Currently, deep learning-based underwater crack detection methods rely heavily on a large number of crack images that are difficult to collect due to their complex and hazardous underwater environments. This study proposes a new underwater image-processing method that combines a novel white balance method and bilateral filtering denoising method to transform underwater crack images into high-quality above-water images with original crack features. Crack detection is then performed based on an improved YOLOv9-OREPA model. Through experiments, it is found that the new image-processing method proposed in this study significantly improves the evaluation indicators of new images, compared with other methods. The improved YOLOv9-OREPA also exhibits a significantly improved performance. The experimental results demonstrate that the method proposed in this study is a new approach suitable for detecting underwater cracks in dams and achieves the goal of transforming underwater images into above-water images.

Attenuated color channel adaptive correction and bilateral weight fusion for underwater image enhancement

Due to the absorption and scattering of light and the influence of suspended particles, underwater images commonly exhibit color distortions, reduced contrast, and diminished details. This paper proposes an attenuated color channel adaptive correction and bilateral weight fusion approach called WLAB to address the aforementioned degradation issues. Specifically, a novel white balance method is first applied to balance the color channel of the input image. Moreover, a local-block-based fast non-local means method is proposed to obtain a denoised version of the color-corrected image. Then, an adaptive stretching method that considers the histogram's local features to get a contrast-enhanced version of the color-corrected image. Finally, a bilateral weight fusion method is proposed to fuse the above two image versions to obtain an output image with complementary advantages. Experimental studies are conducted on three benchmark underwater image datasets and compared with ten state-of-the-art methods. The results show that WLAB has a significant advantage over the comparative methods. Notably, WLAB exhibits a degree of independence from camera settings and enhances the precision of various image processing applications, including key points and saliency detection. Additionally, it demonstrates commendable adaptability in improving low-light and foggy images.

The genetic basis of the kākāpō structural color polymorphism suggests balancing selection by an extinct apex predator.

The information contained in population genomic data can tell us much about the past ecology and evolution of species. We leveraged detailed phenotypic and genomic data of nearly all living kākāpō to understand the evolution of its feather color polymorphism. The kākāpō is an endangered and culturally significant parrot endemic to Aotearoa New Zealand, and the green and olive feather colorations are present at similar frequencies in the population. The presence of such a neatly balanced color polymorphism is remarkable because the entire population currently numbers less than 250 birds, which means it has been exposed to severe genetic drift. We dissected the color phenotype, demonstrating that the two colors differ in their light reflectance patterns due to differential feather structure. We used quantitative genomics methods to identify two genetic variants whose epistatic interaction can fully explain the species' color phenotype. Our genomic forward simulations show that balancing selection might have been pivotal to establish the polymorphism in the ancestrally large population, and to maintain it during population declines that involved a severe bottleneck. We hypothesize that an extinct apex predator was the likely agent of balancing selection, making the color polymorphism in the kākāpō a "ghost of selection past."

Features of Bioluminescence Dynamics of Photobacterium phosphoreum IMV B-7071

The problem of the prolonged and stable intensity of bioluminescent signals is relevant in the development of any test systems that use biological objects. The aim of this work was to study the features of bioluminescence dynamics of Photobacterium phosphoreum IMV B-7071 in a liquid and on different stationary media. Methods. Bioluminescence studies were performed in liquid, agarose, and cellulose-cotton media. Bacterial suspensions were cultivated at 21°C in the mediums with standard composition. We studied both the background glow and its dynamics under conditions of mixing a liquid medium. Bioluminescence was recorded using digital photography with subsequent image processing of the samples. The measurements of luminescence were made by digital photo or video recording using Olympus digital camera SP560UZ, CANON 700D, and mobile device camera Samsung Galaxy 9 Note with specialized applications for mobile devices "Colorimeter (Lab Tools Apps)" and Camera Color Counter (Keuwsoft) at maximum light sensitivity in the automatic white balance mode at a fixed distance from the sample. Image processing was carried out using ImageJ and Origin Pro. Spectra of bacterial luminescence and its dynamics over time were measured using an LOMO MDR-23 spectrometer in the range of 200–750 nm. Results. The results of the study prove that in aqueous or solid agar and also on cellulose cotton medium, the intensity of bioluminescence of P. phosphoreum gradually increases, reaching a maximum within approximately 2 days, after which it slowly fades. It was established that the bioluminescence of photobacterium P. phosphoreum is a non-stationary process and has characteristic features of temporal dynamics associated with both the dynamics of the oxygen concentration in the environment of bacterial suspensions and the dynamics of the bacterial population density. Analysis of the luminescence spectra of bacteria shows that luminescence occurs mainly in the blue and green regions of the spectrum with luminescence maxima in the range of 460–520 nm, but the ultra-weak glow is also registered in the UV and red spectral ranges. The variability of photobacterial luminescence spectra over time in the spectral ranges of the main luminophores causes color fluctuations between the blue and green ranges. Conclusions. The key parameters of multi-day background and short-term induced bioluminescence dynamics of photobacteria in different environments were clarified, and the certain variability of the spectral characteristics of luminescent radiation over time was shown. The revealed features of the dynamics of the bioluminescence of P. phosphoreum must be taken into account in practical application to assess the toxicity of substances of various nature, as well as in environmental monitoring.

A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather

Obstacles on rail transit significantly compromise operational safety, particularly under dense fog conditions. To address missed and false detections in traditional rail transit detection methods, this paper proposes a multi-scale adaptive YOLO (MSA-YOLO) algorithm. The algorithm incorporates six filters: defog, white balance, gamma, contrast, tone, and sharpen, to remove fog and enhance image quality. However, determining the hyperparameters of these filters is challenging. We employ a multi-scale adaptive module to optimize filter hyperparameters, enhancing fog removal and image quality. Subsequently, YOLO is utilized to detect obstacles on rail transit tracks. The experimental results are encouraging, demonstrating the effectiveness of our proposed method in foggy scenarios.

Enhancing Blue Polymer Light-Emitting Diode Performance by Optimizing the Layer Thickness and the Insertion of a Hole-Transporting Layer.

Polymer light-emitting diodes (PLEDs) hold immense promise for energy-efficient lighting and full-color display technologies. In particular, blue PLEDs play a pivotal role in achieving color balance and reducing energy consumption. The optimization of layer thickness in these devices is critical for enhancing their efficiency. PLED layer thickness control impacts exciton recombination probability, charge transport efficiency, and optical resonance, influencing light emission properties. However, experimental variations in layer thickness are complex and costly. This study employed simulations to explore the impact of layer thickness variations on the optical and electrical properties of blue light-emitting diodes. Comparing the simulation results with experimental data achieves valuable insights for optimizing the device's performance. Our findings revealed that controlling the insertion of a layer that works as a hole-transporting and electron-blocking layer (EBL) could greatly enhance the performance of PLEDs. In addition, changing the active layer thickness could optimize device performance. The obtained results in this work contribute to the development of advanced PLED technology and organic light-emitting diodes (OLEDs).

Spectrofluorimetric and smartphone-based detection methods for determination of gentamicin

This paper presents the development of spectrofluorimetric and smartphone-based detection methods for gentamicin determination using fluorescamine as a reagent. The research included selecting excitation (415 nm) and emission (489 nm) wavelengths, reaction time, and conditions like reagent concentration, and pH of the reaction medium. Moreover, the optimal operating parameters of the smartphone camera, like ISO, white balance, camera shutter, and RGB model channel were selected. Analytical parameters of the developed spectrofluorimetric and smartphone-based methods were estimated including the linear range: 0.04–15.00 mg dm−3 and 0.18–1.20 mg dm−3, respectively, limits of detection and quantification: 0.01 and 0.04 mg dm−3, and 0.06 and 0.18 mg dm−3, respectively, and precision (CV, n = 6): 5.2% and 2.8%, respectively. The proposed approaches were successfully applied to determine gentamicin in pharmaceutical samples. The obtained results were consistent with values declared by manufacturers and satisfactory recovery values, 93.2–113.6% were obtained for both spectrofluorimetric and smartphone-based methods. The developed fluorimetric method with smartphone-based detection provides a low limit of detection specific to spectrofluorimetric methods whereas the measurement system is a simple, easily accessible, compact, and low-cost device. Hence, it can become a competitive alternative to other gentamicin determination methods.Graphical abstract