Search Results (101)

Visual thinking leverages spatial mechanisms in animals for navigation and reasoning. Therefore, given the challenge of abstract mathematics and logic, spatial reasoning-based teaching strategies can be highly effective. Our previous research verified that innovative box-and-ball coloring activities help teach elementary school students complex notions like quantifiers, logical connectors, and dynamic systems. However, given the richness of the activities, correction is slow, error-prone, and demands high attention and cognitive load from the teacher. Moreover, feedback to the teacher should be immediate. Thus, we propose to provide the teacher with real-time help with LLMs. We explored various prompting techniques with and without context—Zero-Shot, Few-Shot, Chain of Thought, Visualization of Thought, Self-Consistency, logicLM, and emotional —to test GPT-4o’s visual, logical, and correction capabilities. We obtained that Visualization of Thought and Self-Consistency techniques enabled GPT-4o to correctly evaluate 90% of the logical–spatial problems that we tested. Additionally, we propose a novel prompt combining some of these techniques that achieved 100% accuracy on a testing sample, excelling in spatial problems and enhancing logical reasoning. Full article

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

This paper presents a novel underwater image enhancement method addressing the challenges of low contrast, color distortion, and detail loss prevalent in underwater photography. Unlike existing methods that may introduce color bias or blur during enhancement, our approach leverages a two-pronged strategy. First, an Efficient Fusion Edge Detection (EFED) module preserves crucial edge information, ensuring detail clarity even in challenging turbidity and illumination conditions. Second, a Multi-scale Color Parallel Frequency-division Attention (MCPFA) module integrates multi-color space data with edge information. This module dynamically weights features based on their frequency domain positions, prioritizing high-frequency details and areas affected by light attenuation. Our method further incorporates a dual multi-color space structural loss function, optimizing the performance of the network across RGB, Lab, and HSV color spaces. This approach enhances structural alignment and minimizes color distortion, edge artifacts, and detail loss often observed in existing techniques. Comprehensive quantitative and qualitative evaluations using both full-reference and no-reference image quality metrics demonstrate that our proposed method effectively suppresses scattering noise, corrects color deviations, and significantly enhances image details. In terms of objective evaluation metrics, our method achieves the best performance in the test dataset of EUVP with a PSNR of 23.45, SSIM of 0.821, and UIQM of 3.211, indicating that it outperforms state-of-the-art methods in improving image quality. Full article

In the context of agricultural modernization and intelligentization, automated fruit recognition is of significance for improving harvest efficiency and reducing labor costs. The variety of fruits commonly planted in orchards and the fluctuations in market prices require farmers to adjust the types of crops they plant flexibly. However, the differences in size, shape, and color among different types of fruits make fruit recognition quite challenging. If each type of fruit requires a separate visual model, it becomes time-consuming and labor intensive to train and deploy these models, as well as increasing system complexity and maintenance costs. Therefore, developing a general visual model capable of recognizing multiple types of fruits has great application potential. Existing multi-fruit recognition methods mainly include traditional image processing techniques and deep learning models. Traditional methods perform poorly in dealing with complex backgrounds and diverse fruit morphologies, while current deep learning models may struggle to effectively capture and recognize targets of different scales. To address these challenges, this paper proposes a general fruit recognition model based on the Multi-Scale Attention Network (MSA-Net) and a Hough Transform localization compensation mechanism. By generating multi-scale feature maps through a multi-scale attention mechanism, the model enhances feature learning for fruits of different sizes. In addition, the Hough Transform ellipse detection compensation mechanism uses the shape features of fruits and combines them with MSA-Net recognition results to correct the initial positioning of spherical fruits and improve positioning accuracy. Experimental results show that the MSA-Net model achieves a precision of 97.56, a recall of 92.21, and an [email protected] of 94.81 on a comprehensive dataset containing blueberries, lychees, strawberries, and tomatoes, demonstrating the ability to accurately recognize multiple types of fruits. Moreover, the introduction of the Hough Transform mechanism reduces the average localization error by 8.8 pixels and 3.5 pixels for fruit images at different distances, effectively improving the accuracy of fruit localization. Full article

Background: The choice of the correct color is of paramount importance in esthetic dentistry; however, there is still no consensus on the best technique to determine it. The aim of the present study is to compare the accuracy of a recently introduced colorimeter in shade matching with human vision. In addition, possible variables affecting color-matching by human eye have been analysed. Methods: 18 disc-shaped composite samples with identical size and shape were produced from a composite flow system (Enamel plus HriHF, Micerium): Nine were considered control samples (UD 0-UD 6), and nine were test samples with identical flow composite shade to the control ones. Parallelly, 70 individuals (dental students and dental field professionals) were individually instructed to sit in a dark room illuminated with D55 light and to perform visual shade matching between control and test discs. An error matrix containing ΔE₉₄ between control and test discs was generated, containing four match-clusters depending on perceptibility and acceptability thresholds. The frequency and severity of errors were examined. Results: The colorimeter achieved a 100% perfect matching, while individuals only achieved a 78%. A higher occurrence of mismatches was noted for intermediate composite shades without a statistically significant difference. No statistically significant differences were reported for age, sex, and experience. A statistically significant difference was present among the Optishade match and the visual determination. Conclusions: The instrumental shade-matching evaluation proved to be significantly more reliable than the human visual system. Further research is needed to determine whether the same outcomes are achieved in a clinical setting directly on patients. Full article

Underwater object detection and classification pose significant challenges due to environmental factors such as water turbidity and variable lighting conditions. This research proposes a novel approach that integrates advanced imaging techniques with diffusion models to address these challenges effectively, aligning with Sustainable Development Goal (SDG) 14: Life Below Water. The methodology leverages the Convolutional Block Attention Module (CBAM), Modified Swin Transformer Block (MSTB), and Diffusion model to enhance the quality of underwater images, thereby improving the accuracy of object detection and classification tasks. This study utilizes the TrashCan dataset, comprising diverse underwater scenes and objects, to validate the proposed method’s efficacy. This study proposes an advanced imaging technique YOLO (you only look once) network (AIT-YOLOv7) for detecting objects in underwater images. This network uses a modified U-Net, which focuses on informative features using a convolutional block channel and spatial attentions for color correction and a modified swin transformer block for resolution enhancement. A novel diffusion model proposed using modified U-Net with ResNet understands the intricate structures in images with underwater objects, which enhances detection capabilities under challenging visual conditions. Thus, AIT-YOLOv7 net precisely detects and classifies different classes of objects present in this dataset. These improvements are crucial for applications in marine ecology research, underwater archeology, and environmental monitoring, where precise identification of marine debris, biological organisms, and submerged artifacts is essential. The proposed framework advances underwater imaging technology and supports the sustainable management of marine resources and conservation efforts. The experimental results demonstrate that state-of-the-art object detection methods, namely SSD, YOLOv3, YOLOv4, and YOLOTrashCan, achieve mean accuracies ([email protected]) of 57.19%, 58.12%, 59.78%, and 65.01%, respectively, whereas the proposed AIT-YOLOv7 net reaches a mean accuracy ([email protected]) of 81.4% on the TrashCan dataset, showing a 16.39% improvement. Due to this improvement in the accuracy and efficiency of underwater object detection, this research contributes to broader marine science and technology efforts, promoting the better understanding and management of aquatic ecosystems and helping to prevent and reduce the marine pollution, as emphasized in SDG 14. Full article

Bayesian tensor decomposition has been widely applied in channel parameter estimations, particularly in cases with the presence of interference. However, the types of interference are not considered in Bayesian tensor decomposition, making it difficult to accurately estimate the interference parameters. In this paper, we present a robust tensor variational method using a CANDECOMP/PARAFAC (CP)-based additive interference model for multiple input–multiple output (MIMO) with orthogonal frequency division multiplexing (OFDM) systems. A more realistic interference model compared to traditional colored noise is considered in terms of co-channel interference (CCI) and front-end interference (FEI). In contrast to conventional algorithms that filter out interference, the proposed method jointly estimates the channel and interference parameters in the time–frequency domain. Simulation results validate the correctness of the proposed method by the evidence lower bound (ELBO) and reveal the fact that the proposed method outperforms traditional information-theoretic methods, tensor decomposition models, and robust model based on CP (RCP) in terms of estimation accuracy. Further, the interference parameter estimation technique has profound implications for anti-interference applications and dynamic spectrum allocation. Full article

Exploring and monitoring underwater environments pose unique challenges due to water’s complex optical properties, which significantly impact image quality. Challenges like light absorption and scattering result in color distortion and decreased visibility. Traditional underwater image acquisition methods face these obstacles, highlighting the need for advanced techniques to solve the image color shift and image detail loss caused by the underwater environment in the image enhancement process. This study proposes a salient region-guided underwater image enhancement fusion method to alleviate these problems. First, this study proposes an advanced dark channel prior method to reduce haze effects in underwater images, significantly improving visibility and detail. Subsequently, a comprehensive RGB color correction restores the underwater scene’s natural appearance. The innovation of our method is that it fuses through a combination of Laplacian and Gaussian pyramids, guided by salient region coefficients, thus preserving and accentuating the visually significant elements of the underwater environment. Comprehensive subjective and objective evaluations demonstrate our method’s superior performance in enhancing contrast, color depth, and overall visual quality compared to existing methods. Full article

This study presents a method to enhance the contrast and luminosity of fundus images with boundary reflection. In this work, 100 retina images taken from online databases are utilized to test the performance of the proposed method. First, the red, green and blue channels are read and stored in separate arrays. Then, the area of the eye also called the region of interest (ROI) is located by thresholding. Next, the ratios of R to G and B to G at every pixel in the ROI are calculated and stored along with copies of the R, G and B channels. Then, the RGB channels are subjected to average filtering using a 3 × 3 mask to smoothen the RGB values of pixels, especially along the border of the ROI. In the background brightness estimation stage, the ROI of the three channels is filtered by binomial filters (BFs). This step creates a background brightness (BB) surface of the eye region by levelling the foreground objects like blood vessels, fundi, optic discs and blood spots, thus allowing the estimation of the background illumination. In the next stage, using the BB, the luminosity of the ROI is equalized so that all pixels will have the same background brightness. This is followed by a contrast adjustment of the ROI using CLAHE. Afterward, details of the adjusted green channel are enhanced using information from the adjusted red and blue channels. In the color correction stage, the intensities of pixels in the red and blue channels are adjusted according to their original ratios to the green channel before the three channels are reunited. The resulting color image resembles the original one in color distribution and tone but shows marked improvement in luminosity and contrast. The effectiveness of the approach is tested on the test images and enhancement is noticeable visually and quantitatively in greyscale and color. On average, this method manages to increase the contrast and luminosity of the images. The proposed method was implemented using MATLAB R2021b on an AMD 5900HS processor and the average execution time was less than 10 s. The performance of the filter is compared to those of two other filters and it shows better results. This technique can be a useful tool for ophthalmologists who perform diagnoses on the eyes of diabetic patients. Full article

Due to the detrimental impact of fog on image quality, dehazing maritime images is essential for applications such as safe maritime navigation, surveillance, environmental monitoring, and marine research. Traditional dehazing techniques, which are dependent on presupposed conditions, often fail to perform effectively, particularly when processing sky regions within marine fog images in which these conditions are not met. This study proposes an adaptive sky area segmentation dark channel prior to the marine image dehazing method. This study effectively addresses challenges associated with traditional marine image dehazing methods, improving dehazing results affected by bright targets in the sky area and mitigating the grayish appearance caused by the dark channel. This study uses the grayscale value of the region boundary’s grayscale discontinuity characteristics, takes the grayscale value with the least number of discontinuity areas in the grayscale histogram as a segmentation threshold adapted to the characteristics of the sea fog image to segment bright areas such as the sky, and then uses grayscale gradients to identify grayscale differences in different bright areas, accurately distinguishing boundaries between sky and non-sky areas. By comparing the area parameters, non-sky blocks are filled; this adaptively eliminates interference from other bright non-sky areas and accurately locks the sky area. Furthermore, this study proposes an enhanced dark channel prior approach that optimizes transmittance locally within sky areas and globally across the image. This is achieved using a transmittance optimization algorithm combined with guided filtering technology. The atmospheric light estimation is refined through iterative adjustments, ensuring consistency in brightness between the dehazed and original images. The image reconstruction employs calculated atmospheric light and transmittance values through an atmospheric scattering model. Finally, the use of gamma-correction technology ensures that images more accurately replicate natural colors and brightness levels. Experimental outcomes demonstrate substantial improvements in the contrast, color saturation, and visual clarity of marine fog images. Additionally, a set of foggy marine image data sets is developed for monitoring purposes. Compared with traditional dark channel prior dehazing techniques, this new approach significantly improves fog removal. This advancement enhances the clarity of images obtained from maritime equipment and effectively mitigates the risk of maritime transportation accidents. Full article

The generation of terahertz radiation via laser-induced plasma from two-color femtosecond pulses in air has been extensively studied due to its broad emission spectrum and significant pulse energy. However, precise control over the temporal properties of these ultra-broadband terahertz pulses, as well as the measurement of their polarization state, remain challenging. In this study, we review our latest findings on these topics and present additional results not previously reported in our earlier works. First, we investigate the impact of chirping on the fundamental wave and the effect of manipulating the phase difference between the fundamental wave and the second-harmonic wave on the properties of generated terahertz pulses. We demonstrate that we can tune the time shape of terahertz pulses, causing them to reverse polarity or become bipolar by carefully selecting the correct combination of chirp and phase. Additionally, we introduce a novel technique for polarization characterization, termed terahertz unipolar polarimetry, which utilizes a weak probe beam and avoids the systematic errors associated with traditional methods. This technique is effective for detecting polarization-structured terahertz beams and the longitudinal component of focused terahertz beams. Our findings contribute to the improved control and characterization of terahertz radiation, enhancing its application in fields such as nonlinear optics, spectroscopy, and microscopy. Full article

Underwater imagery plays a vital role in ocean development and conservation efforts. However, underwater images often suffer from chromatic aberration and low contrast due to the attenuation and scattering of visible light in the complex medium of water. To address these issues, we propose an underwater image enhancement network called CM-Net, which utilizes color mapping techniques to remove noise and restore the natural brightness and colors of underwater images. Specifically, CM-Net consists of a three-step solution: adaptive color mapping (ACM), local enhancement (LE), and global generation (GG). Inspired by the principles of color gamut mapping, the ACM enhances the network’s adaptive response to regions with severe color attenuation. ACM enables the correction of the blue-green cast in underwater images by combining color constancy theory with the power of convolutional neural networks. To account for inconsistent attenuation in different channels and spatial regions, we designed a multi-head reinforcement module (MHR) in the LE step. The MHR enhances the network’s attention to channels and spatial regions with more pronounced attenuation, further improving contrast and saturation. Compared to the best candidate models on the EUVP and UIEB datasets, CM-Net improves PSNR by 18.1% and 6.5% and SSIM by 5.9% and 13.3%, respectively. At the same time, CIEDE2000 decreased by 25.6% and 1.3%. Full article

Salivary pH is one of the crucial biomarkers used for non-invasive diagnosis of intraoral diseases, as well as general health conditions. However, standard pH sensors are usually too bulky, expensive, and impractical for routine use outside laboratory settings. Herein, a miniature hydrogel sensor, which enables quick and simple colorimetric detection of pH level, is shown. The sensor structure was manufactured from non-toxic hydrogel ink and patterned in the form of a matrix with 5 mm × 5 mm × 1 mm individual sensing pads using a 3D printing technique (bioplotting). The authors’ ink composition, which contains sodium alginate, polyvinylpyrrolidone, and bromothymol blue indicator, enables repeatable and stable color response to different pH levels. The developed analysis software with an easy-to-use graphical user interface extracts the R(ed), G(reen), and B(lue) components of the color image of the hydrogel pads, and evaluates the pH value in a second. A calibration curve used for the analysis was obtained in a pH range of 3.5 to 9.0 using a laboratory pH meter as a reference. Validation of the sensor was performed on samples of artificial saliva for medical use and its mixtures with beverages of different pH values (lemon juice, coffee, black and green tea, bottled and tap water), and correct responses to acidic and alkaline solutions were observed. The matrix of square sensing pads used in this study provided multiple parallel responses for parametric tests, but the applied 3D printing method and ink composition enable easy adjustment of the shape of the sensing layer to other desired patterns and sizes. Additional mechanical tests of the hydrogel layers confirmed the relatively high quality and durability of the sensor structure. The solution presented here, comprising 3D printed hydrogel sensor pads, simple colorimetric detection, and graphical software for signal processing, opens the way to development of miniature and biocompatible diagnostic devices in the form of flexible, wearable, or intraoral sensors for prospective application in personalized medicine and point-of-care diagnosis. Full article

Malignant skin cancers are common in emerging countries, with excessive sun exposure and genetic predispositions being the main causes. Variations in lighting and color, resulting from the diversity of devices and lighting conditions during image capture, pose a challenge for automated diagnosis through digital images. Deep learning techniques emerge as promising solutions to improve the accuracy of identifying malignant skin lesions. This work aims to investigate the impact of lighting and color correction methods on automated skin cancer diagnosis using deep learning architectures, focusing on the relevance of these characteristics for accuracy in identifying malignant skin cancer. The developed methodology includes steps for hair removal, lighting, and color correction, defining the region of interest, and classification using deep neural network architectures. We employed deep learning techniques such as LCDPNet, LLNeRF, and DSN for lighting and color correction, which still need to be tested in this context. The results emphasize the importance of image preprocessing, especially in lighting and color adjustments, where the best results show an accuracy increase of between 3% and 4%. We observed that different deep neural network architectures react variably to lighting and color corrections. Some architectures are more sensitive to variations in these characteristics, while others are more robust. Advanced lighting and color correction can thus significantly improve the accuracy of malignant skin cancer diagnosis. Full article

We present the application of QR Codes as carriers for colorimetric dyes, whereby this refined version of machine-readable patterns applied to colorimetric sensing also allows us to maintain the data from the QR Code standard in a back-compatible way, which means that the QR Code is still able to encode digital data (readable with a standard QR Code decoder) alongside a hundred colorimetric references and the dyes. Also, we discuss in detail the effectiveness of different color correction methods in attaining color accuracy levels suited for sensing via colorimetry. Moreover, we illustrate how color correction techniques can be applied to take advantage of having hundreds of color references, with an exemplary case of a CO₂ printed sensor used to monitor the integrity of modified atmosphere packaging (MAP). Full article