Image Pixels Research Articles

The implementation of algebraic complex lookup tables over non-chain Galois ring extensions and Henon map in multimedia security

Abstract Image encryption is crucial for web-based data storage and transmission. Complex algebraic structures play a vital role in providing unique features and binary operations. However, current algebraic-based techniques face challenges due to limited key space. To tackle this issue, our study uniquely connects the algebraic structures with a chaotic map. The study introduces a complex non-chain Galois ring structure and a 12-bit substitution box for image substitution. An affine map is utilized to permute image pixels, and the 12-bit substitution box is uniquely mapped to a Galois field for encryption. A two-dimensional Henon map is employed to generate different keys for the XOR operation, resulting in an encrypted image. The resilience of the scheme against various attacks is evaluated using statistical, differential, and quality measures, showcasing its effectiveness against well-known attacks.

MRA-VSS: A Matrix-Based Reversible and Authenticable Visual Secret-Sharing Scheme Using Dual Meaningful Images

Reversible data hiding (RDH) is an approach that emphasizes the imperceptibility of hidden confidential data and the restoration of the original cover image. To achieve these objectives at the same time, in this paper, we design a matrix-based crossover data hiding strategy and then propose a novel matrix-based RDH scheme with dual meaningful image shadows, called MRA-VSS (matrix-based reversible and authenticable visual secret-sharing). Each pixel in a secret image is divided into two parts, and each part is embedded into a cover pixel pair by referring to the intersection point of four overlapping frames. During the share construction phase, not only partial information of the pixel in a secret image but also authentication codes are embedded into the corresponding cover pixel pair. Finally, two meaningful image shadows are derived. The experimental results confirm that our designed MRA-VSS successfully embeds pixels’ partial information and authentication code into cover pixel pairs at the cost of slight distortion during data hiding. Nevertheless, the robustness of our scheme under the steganalysis attack and the authentication capability of our scheme are also proven.

Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology

Color is a key indicator for evaluating the quality of tea during processing; various processing procedures can significantly affect the content of fat-soluble pigments of tea, which in turn affects the color and quality of finished tea. Therefore, there is an urgent demand for the fast, non-destructive detection of pigments of stacked tea during processing. This paper presents the use of hyperspectral imaging technology (HSI), combined with machine learning algorithms, to detect chlorophyll a, chlorophyll b, and carotenoids in stacked matcha tea during processing. Firstly, a quantitative relationship between HSI data of tea and their pigment contents was developed based on regression analysis, and the results showed that exceptional prediction performance was achieved by the partial least squares regression (PLSR) algorithm combined with the feature band algorithm of competitive adaptive reweighting (CARS), and the Rp2 values of detection models of chlorophyll a, chlorophyll b and carotenoids were 0.90465, 0.92068 and 0.62666, respectively. Then, these quantitative detection models were extended to each pixel in hyperspectral images, achieving point-by-point prediction of pigment components, so the distribution of pigments of stacked tea leaves during processing procedures was successfully visualized on the processing line in situ. By integrating a hyperspectral imaging system into the real-world environment, operators can monitor pigment levels in real time and thus dynamically adjust processing parameters based on real-time data. This study enhances pigment detection efficiency in tea processing, supports process optimization, and aids in quality control.

Asymmetric Large Kernel Distillation Network for efficient single image super-resolution

Recently, significant advancements have been made in the field of efficient single-image super-resolution, primarily driven by the innovative concept of information distillation. This method adeptly leverages multi-level features to facilitate high-resolution image reconstruction, allowing for enhanced detail and clarity. However, many existing approaches predominantly emphasize the enhancement of distilled features, often overlooking the critical aspect of improving the feature extraction capabilities of the distillation module itself. In this paper, we address this limitation by introducing an asymmetric large-kernel convolution design. By increasing the size of the convolution kernel, we expand the receptive field, which enables the model to more effectively capture long-range dependencies among image pixels. This enhancement significantly improves the model's perceptual ability, leading to more accurate reconstructions. To maintain a manageable level of model complexity, we adopt a lightweight architecture that employs asymmetric convolution techniques. Building on this foundation, we propose the Lightweight Asymmetric Large Kernel Distillation Network (ALKDNet). Comprehensive experiments conducted on five widely recognized benchmark datasets-Set5, Set14, BSD100, Urban100, and Manga109-indicate that ALKDNet not only preserves efficiency but also demonstrates performance enhancements relative to existing super-resolution methods. The average PSNR and SSIM values show improvements of 0.10 dB and 0.0013, respectively, thereby achieving state-of-the art performance.

Enhanced YOLOv8-Based Model with Context Enrichment Module for Crowd Counting in Complex Drone Imagery

Crowd counting in aerial images presents unique challenges due to varying altitudes, angles, and cluttered backgrounds. Additionally, the small size of targets, often occupying only a few pixels in high-resolution images, further complicates the problem. Current crowd counting models struggle in these complex scenarios, leading to inaccurate counts, which are crucial for crowd management. Moreover, these regression-based models only provide the total count without indicating the location or distribution of people within the environment, limiting their practical utility. While YOLOv8 has achieved significant success in detecting small targets within aerial imagery, it faces challenges when directly applied to crowd counting tasks in such contexts. To overcome these challenges, we propose an improved framework based on YOLOv8, incorporating a context enrichment module (CEM) to capture multiscale contextual information. This enhancement improves the model’s ability to detect and localize tiny targets in complex aerial images. We assess the effectiveness of the proposed framework on the challenging VisDrone-CC2021 dataset, and our experimental results demonstrate the effectiveness of this approach.

Embedding Quantum Random Phase Encoding Arnold Transform for Advanced Image Security

Purpose: This research proposes an improvised version of the image encryption technique by incorporating Quantum Random Phase Encoding with the Arnold Transform to help enhance the strength and non-predictability of the encryption process. In this research work, some ideas gained from quantum-based methods have been brought to use with conventional approaches in image encryption techniques for enhancing their security. Methods: This model represents the basic methodology that underlies the Arnold Transform for scrambling the arrangement of image pixels to mask recognizable structures within quantum random phase encoding to introduce complexity through quantum-generated random phases. Result: The experimental results show much improvement in encryption efficiency. For example, in the case of "Cameraman" and "Lena", MSE parameters are 98.134 and 104.76, respectively; these now go up to 832.01 and 888.78. This implies that the higher decrement of these values 21.17 dB and 23.98 dB to 13.41 dB and 13.33 dB translates into higher distortion with higher security. Meanwhile, UACI and NPCR are also very steady and the mean value is about 0.3356 to 0.3358 and 99.60 to 99.61, which proves that this method has been effective in changing the pixel's value, and sensitive input changes. Novelty: This work is novel due to the introduction of quantum technologies in the classical methodology of image encryption. While classical techniques make use of conventional transforms for scrambling, like the Arnold Transform, this work embeds quantum randomness and intricacy in the process as a means of encoding namely, Quantum Random Phase Encoding.

TRUST-DART: Land Surfaces Temperature and Emissivity Nonlinear Mapping from Nonisothermal Mixed Pixels of Satellite Images with the DART 3D Radiative Transfer Model

Abstract. Coarse spatial resolution of Thermal Infrared (TIR) satellites hampers measuring the temperature and emissivity of scene elements from space that improves our understanding of land surfaces' thermal behavior. The stringent conditions of current TIR unmixing methods hinder the production of extensive component temperature and emissivity products. To address this, we designed a gradient-based multi-pixel physical model, TRUST-DART, to derive the temperature and emissivity of urban features from non-isothermal mixed pixels of satellite images using the DART 3D radiative transfer model. Unlike traditional TIR unmixing methods, TRUSTDART is not constrained by issues related to spatial, spectral, temporal resolution, angular, scene, field measurement requirements, or manual operations. Its inputs include an at-surface radiance image, downwelling sky irradiance, a 3D urban mock-up with feature information, and DART input parameters such as spatial resolution. It generates maps of emissivity and temperature per urban feature. Its accuracy is validated for two vegetation and urban scenes and two types of images (DART simulated pseudo satellite and ASTER observed images). The accuracy of the TRUST-DART depends heavily on the fraction of components. TRUST-DART proves robust for high-fraction components. However, its accuracy decreases with decreasing fractions. TRUST-DART is distributed with DART and is available for education and research via Toulouse III University (https://dart.omp.eu).

Automatic detection of landslide impact areas using Google Earth Engine

This research introduces an effective framework for automatically detecting landslide impact areas using Google Earth Engine (GEE). The Asia–Pacific region frequently experiences earthquakes and heavy rainfall, leading to frequent landslides that cause loss of life and property. Focusing on landslide catalogues from Taiwan and Japan, the study proposes an automatic landslide detection process using a new method termed multi-bitemporal images (MBTIs), which involves the collection of accumulated changes over time. First, set the event date and collect all images before and after the event. Second, analyse the change pixels in bi-temporal images. Third, review all change pixels to determine the total amount of accumulated changes. This method includes all bi-temporal image sets in the analysis, unlike traditional methods that only use single pairs of bi-temporal images. Clouds are filtered using a pixel-based approach and machine learning techniques. The landslide areas are analysed statistically, and appropriate thresholds for automatic landslide detection are suggested. Using reproducibility, which indicates the percentage of bi-temporal images that detect vegetation loss in mountainous areas, the proposed method achieves a 99% reduction in false positives with a reproducibility requirement of 24.21%, while maintaining true positives at 66.89%. This study analyzed 28–720 bi-temporal image sets from various regions using Sentinel-2 data, revealing that subsequent landslides can be 7–293 times larger than co-seismic landslides. In comparison, subsequent landslides were found to be 3–12 times larger than rainfall-induced landslides. Additionally, the impact of earthquake event on subsequent landslides is 2.3–24.4 times greater than that of rainfall-induced event. By using GEE, the accumulation of hundreds of satellite images can be completed within 15 min, depending on the processing requirements.

Data Preprocessing Methods for Selective Sweep Detection using Convolutional Neural Networks.

The identification of positive selection has been framed as a classification task, with Convolutional Neural Networks (CNNs) already outperforming summary statistics and likelihood-based approaches in accuracy. Despite the prevalence of CNN-based methods that manipulate the pixels of images representing raw genomic data as a preprocessing step to improve classification accuracy, the efficacy of these pixel-rearrangement techniques remains inadequately examined, particularly in the presence of confounding factors like population bottlenecks, migration and recombination hotspots. We introduce a set of pixel rearrangement algorithms aimed at enhancing CNN classification accuracy in detecting selective sweeps. These algorithms are employed to assess the performance of four CNN models for selective sweep detection. Our findings illustrate that the judicious application of rearrangement algorithms notably enhances the overall classification accuracy of a CNN across various datasets simulating confounding factors. We observed that sorting the columns of the genomic matrices has higher on CNN performance than rearranging the sequences. To some extent, these rearrangement algorithms are more robust to misspecified demographic models compared with the utilization of the default preprocessing algorithm as suggested by the respective authors of each CNN architecture. We provide the data rearrangement algorithms as a distinct package available for download at: https://github.com/Zhaohq96/Genetic-data-rearrangement.

A perception-guided CNN for grape bunch detection

Precision Viticulture (PV) is becoming an active and interdisciplinary research field since it requires solving interesting research issues to concretely answer the demands of specific use cases. A challenging problem in this context is the development of automatic methods for yield estimation. Computer vision methods can contribute to the accomplishment of this task, especially those that can replicate what winemakers do manually. In this paper, an automatic artificial intelligence method for grape bunch detection from RGB images is presented. A customized Convolutional Neural Network (CNN) is employed for pointwise classification of image pixels and the dependence of classification results on the type of input color channels and grapes color properties are studied. The advantage of using additional perception-based input features, such as luminance and visual contrast, is also evaluated, as well as the dependence of the method on the choice of the training set in terms of the amount of labeled data. The latter point has a significant impact on the practical use of the method on-site, its usability by non-expert users, and its adaptability to individual vineyards. Experimental results show that a properly trained CNN can discriminate and detect grape bunches even under uncontrolled acquisition conditions and with limited computational load, making the proposed method implementable on smart devices and suitable for on-site and real-time applications.

Deep adversarial learning models for distribution patterns of piezoelectric plate energy harvesting

This paper presents a novel approach utilizing piezoelectric plate structures with random electrode distribution patterns for energy harvesting applications across various vibration modes. For the first time, leveraging electromechanical Finite Element Analysis (eFEA) and data extraction techniques, we investigate the integration of conditional Generative Adversarial Networks (cGAN)-based dynamic models for this purpose. The cGAN offers an effective technique for generating realistic synthetic data conditioned on input parameters, thereby enabling the creation of diverse and representative datasets for training energy harvesting systems. The integration of eFEA with cGAN opens up new possibilities for optimizing the design and performance of piezoelectric energy harvesters across various applications. Specifically, we explore four distinct cGAN models-based mechanics of energy harvesters by deploying distribution patterns. These models include training data generated by stacking simultaneously mode images, utilizing separate cGAN models for each mode, labeling images by mode, and concatenating all mode images into one. Our study focuses on assessing the effectiveness of these models in minimizing loss in cGAN-based power generation and predicting Structural Similarity Index Measure (SSIM) values, and more importantly, identifying the predicted data point outputs from the generated pixel image extractions. By analyzing the generated data from numerical model and its application in deep learning, we aim to enhance the understanding of the effects of distribution patterns and image processing techniques for optimal power generation and the effectiveness of piezoelectric energy harvesting systems across different vibration modes. The studies explore how different distribution patterns affect the power harvesting efficiency and frequency bandwidth, utilizing the generated datasets.

A growth posture identification method of immature peaches in natural environments

The precise bagging of immature peaches by the fruit bagging robot requires the identification of the target young fruits as well as the acquisition of their growth angles. The network models employed in single detection algorithms exhibit complexity and pose challenges for deployment on mobile terminals as they heavily rely on the availability of labeled samples. A semi-supervised learning and lightweight strategy based on YOLOv8n-seg was proposed to identify the growth posture of immature peaches in the work. Firstly, the self-training method and data enhancement in semi-supervised learning were used to generate efficient pseudo-label data. A tremendous labeling workload was solved. Secondly, the YOLOv8n-seg backbone network was replaced with an improved MobileNetv3 structure for better real-time detection on MT to reduce network parameters and calculations. The model was easily deployed with improved detection speed. Meanwhile, the original upsampling module was replaced with the CARAFE module to enhance the recognition capability of global features, which considered the impact of immature peaches on the nearby color background. The CIOU loss function was ultimately substituted with the SIOU loss function to further optimize the boundary frame loss and target detection accuracy. The enhanced model could predict the coordinate information of immature peaches and calculate growth angles. The experimental results show that the improved peach seedling growth posture network model has a weight size of only 23.1% of the original model. Additionally, the algorithm achieved a remarkable rate of 87.8% in identifying young peach fruits, with an average error of ±3.3° in estimating the growth angle. It took an average of 31 ms to detect a 3024 × 4032 pixels image in the edge computing device JETSON AGX ORIN CLB development kits. The method could rapidly identify immature peaches and estimate the growth angle, which ensured accurate bagging.

Resolution Improvement in Near-Virtual-Image-Mode Light-Field Display Using Resolution-Priority Technique

A light-field display with a near-virtual-image mode, which employs both a lens array and an aperture array, was previously proposed to provide a wide viewing zone angle and bright three-dimensional (3D) images. However, it is desirable to enhance its resolutions, which are presently equal to those of conventional displays. Thus, we proposed a technique for increasing the resolutions of 3D images generated by the light-field display with the near-virtual-image mode. The gap between the flat-panel display and lens array is reduced to decrease the magnification of the virtual images of the pixels and to enable the observation of multiple virtual pixel images through each lens. Further, we imaged the aperture array using the lens array to eliminate the gaps between the multiple pixels observed through adjacent lenses. We constructed a prototype display based on the proposed technique and verified the increase in the resolution of the prototype display compared to the original near-virtual-image light-field display.

Automated Recognition of Submerged Body-like Objects in Sonar Images Using Convolutional Neural Networks

The Police Robot for Inspection and Mapping of Underwater Evidence (PRIME) is an uncrewed surface vehicle (USV) currently being developed for underwater search and recovery teams to assist in crime scene investigation. The USV maps underwater scenes using sidescan sonar (SSS). Test exercises use a clothed mannequin lying on the seafloor as a target object to evaluate system performance. A robust, automated method for detecting human body-shaped objects is required to maximise operational functionality. The use of a convolutional neural network (CNN) for automatic target recognition (ATR) is proposed. SSS image data acquired from four different locations during previous missions were used to build a dataset consisting of two classes, i.e., a binary classification problem. The target object class consisted of 166 196 × 196 pixel image snippets of the underwater mannequin, whereas the non-target class consisted of 13,054 examples. Due to the large class imbalance in the dataset, CNN models were trained with six different imbalance ratios. Two different pre-trained models (ResNet-50 and Xception) were compared, and trained via transfer learning. This paper presents results from the CNNs and details the training methods used. Larger datasets are shown to improve CNN performance despite class imbalance, achieving average F1 scores of 97% in image classification. Average F1 scores for target vs background classification with unseen data are only 47% but the end result is enhanced by combining multiple weak classification results in an ensemble average. The combined output, represented as a georeferenced heatmap, accurately indicates the target object location with a high detection confidence and one false positive of low confidence. The CNN approach shows improved object detection performance when compared to the currently used ATR method.

CloudCatcher: citizen science can be used to help to crowd source a satellite cloud validation dataset

AbstractAccurately identifying clouds in satellite images is an important stage in processing such images for scientific use. Cloudy image pixels are labelled using a pixel mask, to allow them to be filtered out and ignored. The accuracy at which ‘cloud masking’ can be performed needs to be determined and this is often done by expert manual classification. Given the public enthusiasm for weather, we tested whether this classification could be performed reliably by ‘citizen scientists’. We found that the public could identify cloud with a 94% accuracy over ocean. Our analysis pointed to future improvements of the CloudCatcher project.

Implementation of the YOLO Method for Detection of Human Emotions Based on Facial Mimics

Emotion detection through facial expression recognition plays an important role in everyday life, such as how to respond correctly to emotional expressions in social interactions, so that you can establish and build verbal or nonverbal communication with other people and so on. Facial expressions are facial changes in response to a person's emotional state, intentions, or social communication. Face detection is the first step that must be taken in facial analysis, including facial expression recognition. There are many methods that can be used to carry out the face detection process, such as the YOLO method. This YOLO method reframes object detection as a single regression problem, directly from image pixels to bounding box coordinates and class probabilities. By using the YOLO method, the process only needs to look once at the input image, to predict what objects are in the image and where those objects are. Based on the results of the tests carried out, the YOLO method can be used to detect human facial expressions with a success rate of 80%, with neutral, surprise and disgust facial expressions having a good level of accuracy and fear facial expressions having a poor accuracy level. The YOLO method is able to detect facial expressions of humans who wear accessories such as glasses.

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence (AI) interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue.
In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.

Fast Color Image Encryption Algorithm Based on DNA Coding and Multi-Chaotic Systems

At present, there is a growing emphasis on safeguarding image data, yet conventional encryption methods are full of numerous limitations. In order to tackle the limitations of conventional color image encryption methodologies, such as inefficiency and insufficient security, this paper designs an expedited encryption method for color images that uses DNA coding in conjunction with multiple chaotic systems. The encryption algorithm proposed in this paper is based on three-dimensional permutation, global scrambling, one-dimensional diffusion and DNA coding. First of all, the encryption algorithm uses three-dimensional permutation algorithms to scramble the image, which disrupts the high correlation among the image pixels. Second, the RSA algorithm and the SHA-256 hashing algorithm are utilized to derive the starting value necessary for the chaotic system to produce the key. Third, the image is encrypted by using global scrambling and one-dimensional diffusion. Finally, DNA coding rules are used to perform DNA computing. The experimental results indicate that the encryption scheme exhibits a relatively weak inter-pixel correlation, uniform histogram distribution, and an information entropy value approaching eight. This shows that the proposed algorithm is able to protect the image safely and efficiently.

3D Data Integration in the Voxel Domain

Abstract. The interest in 3D data integration is growing as the concept of Spatial Digital Twins gains traction among local governments and stakeholders. Spatial data is typically organized into standardised themes such as buildings, transport, and vegetation, or as ad hoc data products in either 2D or 3D dimensions. However, integrating these data sets into a cohesive 3D model presents several challenges. The foundational data sets may vary in terms of accuracy, resolution, representation, and actuality due to differences in creation standards and procedures. Consequently, inconsistencies often arise within the 3D models, making them difficult to identify and rectify. Many of these data integration issues can be traced back to the 3D vector representation. Vector 3D models exhibit significant geometric diversity, which complicates the computational operations required for examining and validating the models. In this paper, we propose a novel voxel-based approach for integrating 3D data. Voxels, analogous to pixels in 2D raster images, inherit several key properties. We employ a dedicated voxel data structure and a set of operations tailored for this purpose. First, we voxelise vector datasets, converting continuous geometric information into discrete voxel representations. Next, we merge voxel layers using a specialized voxel overlay operation. The resulting 3D model adheres to most validity and integrity requirements. To highlight the flexibility of our voxel data structure, we transformed it into point clouds for visualisation in ArcGIS Pro, enabling the addition of 3D base maps and GIS layers. We then published these datasets to Scene Services, granting public access. This allows users to easily explore the 3D scenes through a web browser, making complex geographic data more accessible. By leveraging voxels, our approach facilitates efficient and accurate 3D data integration, making it a valuable contribution to the field.

A new procedure to find the optimum deconvolution kernel to deblur satellite images

ABSTRACT The digital number of a given pixel in a satellite image does not only measure the radiance originating from the area of the Earth’s surface represented by this pixel, but also measures radiance originating from surrounding areas represented by adjacent pixels. This adjacency effect, also known as image blurring, is a fundamental limitation of satellite images, and introduces errors in all image processing techniques that quantify the properties of the Earth’s surface on a per-pixel basis. Image deblurring via deconvolution, is a traditional technique to reduce the adjacency effect, which has proven effective in a variety of remote sensing applications. For example, it has provided an almost fourfold reduction of the adjacency error, when using support vector machine classifiers to estimate land cover proportion at a subpixel level. The remote sensing literature usually assumes that satellite images should be deconvolved with a kernel defined by the sensor’s Point Spread Function PSF(x, y), overlooking the results of three seminal empirical studies which systematically demonstrated that the optimum deconvolution kernel is a shrunk PSF of the form PSF(x/β, y/β), where β < 1 is the optimum shrink factor. The current empirical procedure to find the optimum shrink factor is laborious and restrictive since it requires suitable satellite images of much higher resolution, such that their Ground Sampling Distance (GSD) is equal to the GSD of the sensor of interest divided by an integer number much greater than one. A new theoretical procedure that uses synthetic edge images to find the optimum shrink factor is proposed and applied to the same cases considered by the current empirical procedure, showing that the same results are obtained. The new procedure is much simpler to apply and more accurate and versatile, opening a variety of research paths on satellite image deblurring.