Image Color Research Articles

Ferrofluid‐Assisted Dynamic Metasurface 3D Holography Endowed With Rapid, Linear, and High‐Contrast Color Modulation

AbstractMetasurface holography offers a highly promising approach for naked‐eye, full‐color, 3D displays. However, the natural static structure and material of the metasurface limit its ability to dynamically regulate the light field, posing a challenge for achieving photorealistic 3D holography. Here, a ferrofluid‐assisted dynamic metasurface is first demonstrated, conducting a 3D holography endowed with rapid, linear, and high‐contrast color modulation. The ferrofluid has nano‐structural control properties (flow behavior of nanoparticles and emulsion) that enable dynamic manipulation of the extinction (absorption and scattering) intensity in micron region across a broadband spectrum range (400–850 nm). By integrating the ferrofluid with a broadband metasurface, a novel dynamic meta‐hologram is created, which can not only regulate both amplitude and phase of the light field under a single wavelength laser but also in situ linearly changing the color of holographic images under multi‐wavelength lasers. This method provides a new perspective for implementing dynamic metasurface 3D holography capable of continuous color response.

Machine Learning Approaches for Binary Classification of Sorghum (Sorghum bicolor L.) Seeds from Image Color Features

Machine Learning Approaches for Binary Classification of Sorghum (Sorghum bicolor L.) Seeds from Image Color Features

Leveraging Convolutional Neural Networks for Face Mask Detection

In response to the widespread use of face masks for safety, security, and health reasons, the development of automated systems for face mask detection has garnered significant attention. This research addresses binary face mask detection by leveraging Convolutional Neural Networks (CNNs) to create a specialized model. Using a Kaggle dataset, preprocessing steps including image resizing and color space conversion are applied for standardized data. The method entails a purpose-built CNN architecture comprising multiple convolutional layers with ReLU (Rectified Linear Unit) activations and max pooling for efficient feature extraction and spatial information capture. Further bolstering the architecture, fully connected layers coupled with dropout layers mitigate overfitting risks, enhancing generalization. The final sigmoid-activated output layer facilitates precise binary classification, distinguishing individuals with or without masks. Training guided by the Adam optimizer ensures parameter optimization based on accuracy metrics. This work contributes a meticulously designed CNN architecture optimized for face mask detection, showcasing robust feature extraction, spatial complexity handling, and overfitting mitigation, thereby presenting a potent solution with broad implications across industries for safety and public health measures. By aligning technology with societal needs, the research aids diverse industries in integrating automation and Artificial Intelligence for mask-wearing compliance. The findings underscore mask detection's pivotal role in overcoming challenges for safer environments.

Extreme learning machine for identifying soil-dwelling microorganisms cultivated on agar media

The aim of this research is to create an automated system for identifying soil microorganisms at the genera level based on raw microscopic images of monocultural colonies grown in laboratory environment. The examined genera are: Fusarium, Trichoderma, Verticillium, Purpureolicillium and Phytophthora. The proposed pipeline deals with unprocessed microscopic images, avoiding additional sample marking or coloration. The methodology includes several stages: image preprocessing, segmenting images to isolate microorganisms from the background, calculating features related to image color and texture for classification. Using an extensive dataset of 2866 images from the National Institute of Horticultural Research in Skierniewice the Extreme Learning Machine model was trained and validated. The model showcases high accuracy and computational efficiency compared to other Machine Learning state-of-the art methods e.g. CatBoost, Random Forest or Convolutional Neural Networks. Statistical techniques, including Multivariate Analysis of Variance were employed to confirm significant differences among the datasets, enhancing the model’s robustness. Nevertheless, Shapley Additive Explanations values provided transparency into the model’s decision-making process. This approach has the potential to improve early detection and management of soil pathogens, promoting sustainable agriculture and demonstrating machine learning’s potential in environmental monitoring, microbial ecology or industrial microbiology.

Image Color Consistency Correction Method Based on Iterative Strategy and Quadratic Convex Optimization

Image Color Consistency Correction Method Based on Iterative Strategy and Quadratic Convex Optimization

Underwater image processing and target detection from particle swarm optimization algorithm

The underwater image obtained is difficult to satisfy human visual perception because of the particle scattering and water absorption phenomena when visible light propagates underwater. In underwater images, light absorption easily leads to image distortion and reduction of image contrast and brightness. Therefore, this work aims to improve the quality of underwater image processing, reduce the distortion rate of underwater images, and further improve the efficiency of underwater image extraction, processing, and tracking. This work combines intelligent blockchain technology in emerging multimedia industries with existing image processing technology to improve the target detection capability of image processing algorithms. Firstly, the theory of visual saliency analysis (VSA) is studied. The steps of image processing using VSA are analyzed. Based on the original Itti model, the visual significance detection step is optimized. Then, the theoretical basis and operation steps of particle swarm optimization (PSO) algorithm in intelligent blockchain technology are studied. VSA theory is combined with PSO to design underwater image processing algorithms and target detection optimization algorithms for underwater images. The experimental results show that: (1) the method has a higher F value and lower Mean Absolute Error. (2) Compared with the original image, the restored image entropy through this method is greatly improved, and the information in the image increases. Therefore, this method has good performance. Besides, this method performs well in image definition, color, and brightness. The quality of the restored image through this method is better than that of other algorithms. (3) Compared with similar algorithms, the relative errors of this method are reduced by 2.56%, 3.24% and 3.89%, respectively. The results show that the method has high accuracy. The research results can provide a reference for future underwater image processing and target detection research. In addition, the designed underwater image processing and target detection and tracking algorithms can improve the detection efficiency and accuracy of underwater targets and help to accurately obtain underwater target images.

Structural stability and thermodynamics of artistic composition

Inspired by the way that digital artists zoom out of the canvas to assess the visual impact of their works, we introduce a conceptually simple yet effective metric for quantifying the clarity of digital images. This metric contrasts original images with progressively "melted" counterparts, produced by randomly flipping adjacent pixel pairs. It measures the presence of stable structures, assigning the value zero to completely uniform or random images and finite values for those with discernible patterns. This metric respects the color diversity of the original image and withstands image compression and color quantization. Its suitability for diverse image analysis problems is demonstrated through its effective evaluation of textural images, the identification of structural transitions in physical systems like the Potts model, and its consistency with color theory in digital arts. This allows us to demonstrate that color in visual art functions as a state variable, akin to the spin configuration in magnets, driving artistic designs to transition between states with distinct clarity. When combined with the Shannon entropy, which quantifies color diversity, the structural stability metric can serve as a navigation tool for artists to explore pathways on the complex structural information landscape toward the completion of their artwork. As a practical demonstration, we apply our metric to refine and optimize an emote design for a video game. The structural stability metric emerges as a versatile tool for extracting nuanced structural information from digital images, which may enhance decision-making and data analysis across scientific and creative domains.

An automated method for progress monitoring of under-ground pipe installation sites using image color analysis of iPhone LiDAR camera data

An automated method for progress monitoring of under-ground pipe installation sites using image color analysis of iPhone LiDAR camera data

Optomagnetic Micromirror Arrays for Mapping Large Area Stiffness Distributions of Biomimetic Materials.

A new device termed "Optomagnetic Micromirror Arrays" (OMA) is demonstrated capable of mapping the stiffness distribution of biomimetic materials across a 5.1 mm × 7.2 mm field of view with cellular resolution. The OMA device comprises an array of 50000 magnetic micromirrors with optical grating structures embedded beneath an elastic PDMS film, with biomimetic materials affixed on top. Illumination of a broadband white light beam onto these micromirrors results in the reflection of microscale rainbow light rays on each micromirror. When a magnetic field is applied, it causes each micromirror to tilt differently depending on the local stiffness of the biomimetic materials. Through imaging these micromirrors with low N.A. optics, a specific narrow band of reflection light rays from each micromirror is captured. Changing a micromirror's tilt angle also alters the color spectrum it reflects back to the imaging system and the color of the micromirror image it represents. As a result, OMA can infer the local stiffness of the biomimetic materials through the color change detected on each micromirror. OMA offers the potential for high-throughput stiffness mapping at the tissue-level while maintaining spatial resolution at the cellular level.

Photorealistic attention style transfer network for architectural photography photos

Architectural photography style transfer, a task in computer vision, employs deep learning algorithms to transform the style of architectural photograph while preserving key structure and content. Existing algorithms face challenges due to the intricate details of buildings, including diverse shapes, lines, and textures. Moreover, considerations for artistic effects in architectural photography style transfer, such as lighting, shadows, and atmosphere, require high-quality image generation algorithms. However, current algorithms often struggle to address these complexities, leading to loss or blurring of details and less realistic images. To overcome these challenges, this paper proposes a Photorealistic Attention Style Transfer Network. The proposed approach utilizes a semantic segmentation model to accurately segment the input image into foreground and background components for independent style transfer. Subsequently, the transferred images are refined by focusing on intricate building parts using the coordinate attention mechanism. Additionally, the network incorporates loss functions to capture light, shadow, and colors in stylish images, ensuring realism while maintaining aesthetic appeal. Through comparative experiments, the proposed network shows better performance in terms of image fidelity and overall aesthetics, and the SSIM and PSNR indices are also better than the current mainstream methods.

Training-free prior guided diffusion model for zero-reference low-light image enhancement

Images captured under poor illumination not only struggle to provide satisfactory visual information but also adversely affect high-level visual tasks. Therefore, we delve into low-light image enhancement. We mainly focus on two practical challenges: (1) previous methods predominantly require supervised training with paired data, tending to learn mappings specific to the training data, which limits their generalization ability on unseen images. (2) existing unsupervised methods usually yield sub-optimal image quality due to the insufficient utilization of image priors. To address these challenges, we propose a training-free Prior Guided Diffusion model, namely PGDiff, for zero-reference low-light image enhancement. Specifically, to leverage the implicit information within the degraded image, we propose a frequency-guided mechanism to obtain low-frequency features through bright channel prior, which combined with the generative prior of the pre-trained diffusion model to recover high-frequency details. To improve the quality of generated images, we further introduce the gradient guidance based on image exposure and color priors. Benefiting from this dual-guided mechanism, PGDiff can produce high-quality restoration results without requiring tedious training or paired reference images. Extensive experiments on paired and unpaired datasets show that our training-free method achieves competitive performance against existing learning-based methods, surpassing the state-of-the-art method QuadPrior by 0.25 dB in PSNR on the LOL dataset.

Robust Single Object Visual Tracking Using Yolo Object Detection and Adaptive Particle Filtering

Visual tracking is a fundamental technology in computer vision, with wide-ranging applications in fields such as robotics, autonomous driving, augmented reality, and security systems. A key challenge in these domains is ensuring stable object tracking in dynamic and complex environments. Tracking objects in real-world settings presents difficulties like occlusion, lighting changes, and sudden object movements, all of which demand the development of robust algorithms that can handle such variability effectively. This research introduces a novel single object visual tracking technique that leverages the YOLO (You Only Look Once) object detection model, combined with an adaptive particle filter. By integrating YOLO's precise object detection capabilities with the continuous state estimation offered by particle filters, this approach achieves stable object tracking, even in challenging environments. At the heart of the proposed algorithm is a dynamic fusion of YOLO detection results with image feature-based particle weight updates. When YOLO successfully detects an object, the position and size data of the object are used to adjust the state and weights of the particles, resulting in improved tracking accuracy and quick recovery from events such as sudden movements or occlusions. Conversely, in situations where YOLO detection is unavailable, the algorithm seamlessly transitions to a pure particle filter mode. In this fallback mode, visual features, such as image color histograms, are used to update particle weights, ensuring continuous and robust tracking even during temporary detection failures. The object’s dynamic state is modeled using an 8-dimensional vector, which includes information about position, velocity, size, and size-change rate. This comprehensive state representation enables precise tracking of various object motions and size transformations. Additionally, a Bhattacharyya distance-based weight calculation method is employed to assess particle similarity more effectively. Experimental results demonstrate that the proposed method excels in various challenging scenarios, including those with sudden object movements, partial occlusions, and lighting variations. The algorithm consistently delivers stable tracking, underscoring its robustness and adaptability. The contributions of this research have potential applications in fields such as autonomous driving, SLAM (Simultaneous Localization and Mapping), and augmented reality. Specifically, this technique could enhance pedestrian and vehicle tracking in autonomous driving systems and improve the accuracy of dynamic object tracking and environmental mapping in SLAM. It is also likely to play a key role in real-world applications like person tracking in security systems, player motion analysis in sports, and focused tracking in medical imaging. Future research directions include expanding the method to handle multi-object tracking and optimizing its real-time performance, broadening its applicability. Additionally, further improvements in tracking accuracy and robustness may be achieved by incorporating deep learning-based feature extraction and integrating data from various sensors.

Detection of motor nervous disease using deep learning based Duple feature extraction network

Background A motor nervous disease (MND) is a debilitating nervous disease that affects motor neurons that regulates the muscular voluntary movements. The disease gradually destroys parts of the neurological system. Generally, MND develops owing to a grouping of genetic, behavioural, and natural features. Objective However, early detection of MND is challenging and manual identification requires a lot of time. Therefore, automated methods like deep learning structures are needed to detect MND quickly and more accurately than manual classification. In this work, a novel deep learning-based Duple feature extraction network is proposed for identifying MND in its early stages. Methods Initially, the input DTI images are pre-processed utilizing a Gaussian adaptive bilateral filter (GAB) to improve the quality of the image. Then the pre-processed DTI images are fed into the dual feature extraction phase for colour and structural conversion. The Colour Information Feature (CIF) with Local and Global sampling (LOG) is integrated into the LinkNet module to extract colour features. Moreover, the Local Binary Pattern (LBP) with Edge sampling models is integrated into the MobileNet module to extract edge features. Afterward, the extracted colour and texture features of images are flattered and given as the input to a Deep Neural Network for classifying the MND levels. Results From the test results, the proposed Duple feature extraction network has yielded a 99.62% accuracy rate. The proposed DNN improves its F1-score by 1.32%, 2.1%, and 3.18% better than FNN, GNN, and GRU respectively. The proposed Duple-feature extraction network improves overall accuracy by 6.15%, 5.56%, 5.96%, and 6.68% compared to CNN, SVM-RFE, MLP, and Tri-planar CNN respectively. Conclusion The novel deep learning-based Duple feature extraction framework shows promising results in early detection of motor nervous disease, significantly improving accuracy and f1-scores compared to existing models.

LM-CycleGAN: Improving Underwater Image Quality Through Learned Perceptual Image Patch Similarity and Multi-Scale Adaptive Fusion Attention.

The underwater imaging process is often hindered by high noise levels, blurring, and color distortion due to light scattering, absorption, and suspended particles in the water. To address the challenges of image enhancement in complex underwater environments, this paper proposes an underwater image color correction and detail enhancement model based on an improved Cycle-consistent Generative Adversarial Network (CycleGAN), named LPIPS-MAFA CycleGAN (LM-CycleGAN). The model integrates a Multi-scale Adaptive Fusion Attention (MAFA) mechanism into the generator architecture to enhance its ability to perceive image details. At the same time, the Learned Perceptual Image Patch Similarity (LPIPS) is introduced into the loss function to make the training process more focused on the structural information of the image. Experiments conducted on the public datasets UIEB and EUVP demonstrate that LM-CycleGAN achieves significant improvements in Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Average Gradient (AG), Underwater Color Image Quality Evaluation (UCIQE), and Underwater Image Quality Measure (UIQM). Moreover, the model excels in color correction and fidelity, successfully avoiding issues such as red checkerboard artifacts and blurred edge details commonly observed in reconstructed images generated by traditional CycleGAN approaches.

Color Reproduction of Chinese Painting Under Multi-Angle Light Source Based on BRDF

It is difficult to achieve high-precision color reproduction using traditional color reproduction methods when the angle is changed, and, for large-sized artefacts, it is also significantly difficult to collect a large amount of data and reproduce the colors. In this paper, we use three Bidirectional Reflectance Distribution Function (BRDF) modeling methods based on spectral imaging techniques, namely, the five-parameter model, the Cook–Torrance model and the segmented linear interpolation model. We investigated the color reproduction of color chips with matte surfaces and Chinese paintings with rough surfaces under unknown illumination angles. Experiments have shown that all three models can effectively perform image reconstruction under small illumination angle intervals. The segmented linear interpolation model exhibits a higher stability and accuracy in color reconstruction under small and large illumination angle intervals; it can not only reconstruct color chips and Chinese painting images under any illumination angle, but also achieve high-quality image color reconstruction standards in terms of objective data and intuitive perception. The best test model (segmented linear interpolation) performs well in reconstruction, reconstructing Chinese paintings at 65° and 125° with an illumination angle interval of 10°. The average RMSE of the selected reference color blocks is 0.0450 and 0.0589, the average CIEDE2000 color difference is 1.07 and 1.50, and the SSIM values are 0.9227 and 0.9736, respectively. This research can provide a theoretical basis and methodological support for accurate color reproduction as well as the large-sized scientific prediction of artifacts at any angle, and has potential applications in cultural relic protection, art reproduction, and other fields.

MACT: Underwater image color correction via Minimally Attenuated Channel Transfer

Underwater images usually show reduced quality due to the underwater environment where light propagation is affected by scattering and absorption, severely limiting the effectiveness of underwater images in practical applications. To effectively deal with the problem of poor underwater image quality, this paper proposes an innovative Minimally Attenuated Channel Transfer (MACT) method that effectively recovers color distortion and enhances the visibility of underwater images. In underwater images captured from natural scenes, specific color channels are often observed to be severely attenuated. To compensate for the information loss caused by channel attenuation, our color correction method selects the channel with the most minor degradation in the degraded image as the reference channel. Subsequently, we employ the reference channel and the color compensation factor obtained by dual-mean difference to perform adaptive color compensation on different color-degraded channels. Finally, we balance the histogram distribution of the compensated color channels by a linear stretching operation. Extensive experimental results on three benchmark datasets demonstrate that our preprocessing method achieves better performance. The project page is available at https://www.researchgate.net/publication/384252681_2024-MACT.

Pengenalan Gambar Dasar Menggunakan Python dan OpenCV

OpenCV is a widely used library in the field of image processing and computer vision. Combined with Python's flexibility, OpenCV provides an extensive range of functions for efficient image processing, modification, analysis, and visualization. This paper aims to introduce the fundamental concepts of image processing using Python and OpenCV, including image reading, color conversion, edge detection, and image manipulation such as resizing and cropping. Furthermore, this study discusses basic analysis techniques like color distribution histograms and feature detection. By presenting these concepts, the paper aspires to help readers understand the foundations of digital image processing and explore the potential applications of OpenCV in practical fields such as pattern recognition, object detection, and image segmentation.

A robust approach for balancing the color and light integrity of underwater images

Abstract As light travels under the deep water, it scatters and is absorbed, resulting in a loss of intensity and altered color perception—a phenomenon known as underwater light attenuation. Images captured under these low light conditions suffered from color distortions, as you go deeper, colors fade in this order: red, orange, and yellow, while green and blue become more prominent. The red channel experiences significant attenuation due to the scattering properties of light under the deep water. As a consequence, deep water images often display noticeable color casts. Researchers encounter various challenges when enhancing low-light underwater images, such as reduced contrast, detail loss, artifacts, noise, and color distortion. In this paper, we present a novel Adaptive Color and Light Correction (ACLC) method for color correction and an Intuitionistic Fuzzy Generator (IFG) for enhancing low-light underwater images. The proposed Adaptive Color and Light Correction (ACLC) method tackles color casts on individual pixels by considering the scene depth. The proposed Intuitionistic Fuzzy Generator (IFG) method balances the image contrast by computing an intuitionistic fuzzy image representation using the proposed IFG approach. Experimental results reveal that the proposed approach significantly improves the color quality and contrast of the output image. The proposed ACLC and IFG methods exceed existing underwater color correction and low-light image enhancement techniques in visual and quantitative evaluations, as evidenced by extensive experimentation on well-established underwater image datasets, such as UIEB, Ocean dark, and LSUI.

Relationship between high-level color features and temperature mapping of magnesium alloy surface images based on the K-nearest neighbor algorithm

Numerous deformation and processing application scenarios of magnesium alloy sheets require high precision and efficiency in temperature detection. Traditional contact temperature measurement and infrared thermal imaging temperature measurement methods suffer from low accuracy and susceptibility to environmental influences. To this end, a real-time sensing method for determining the average surface temperature of magnesium alloys using visible light images is proposed in this paper. Firstly, real-time surface images of 3 mm thick magnesium alloy sheets were captured as the temperature decreased from 450 °C to 300 °C under varying light intensities. Then, calculate the high-level color features based on the grayscale frequency of the Red Green Blue color space of the image, and select the high-level color features with a strong correlation with temperature by Fisher criterion. Finally, a mapping relationship between the high-level color features of magnesium alloy images and temperature was established using the K-nearest neighbor algorithm. The results show that images in RAW format have a wider grayscale range compared to JPG, which is helpful in determining the mapping relationship between image color features and temperature. After extracting and selecting the high-level color features that have a strong correlation with temperature, the dimensions of input features are reduced. Using the K-nearest neighbor algorithm can fit the complex nonlinear relationship between high-level color features and temperature more accurately.

Image enhancement with art design: a visual feature approach with a CNN-transformer fusion model.

Graphic design, as a product of the burgeoning new media era, has seen its users' requirements for images continuously evolve. However, external factors such as light and noise often cause graphic design images to become distorted during acquisition. To enhance the definition of these images, this paper introduces a novel image enhancement model based on visual features. Initially, a histogram equalization (HE) algorithm is applied to enhance the graphic design images. Subsequently, image feature extraction is performed using a dual-flow network comprising convolutional neural network (CNN) and Transformer architectures. The CNN employs a residual dense block (RDB) to embed spatial local structure information with varying receptive fields. An improved attention mechanism module, attention feature fusion (AFF), is then introduced to integrate the image features extracted from the dual-flow network. Finally, through image perception quality guided adversarial learning, the model adjusts the initial enhanced image's color and recovers more details. Experimental results demonstrate that the proposed algorithm model achieves enhancement effects exceeding 90% on two large image datasets, which represents a 5%-10% improvement over other models. Furthermore, the algorithm exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) image quality evaluation metrics. Our findings indicate that the fusion model significantly enhances image quality, thereby advancing the field of graphic design and showcasing its potential in cultural and creative product design.