Gray Images Research Articles

Visibility of alveolar bone thicknesses on CBCT images-a study on minimum bone requirements using various reconstruction techniques, viewing modes, and resolutions.

To evaluate at which thickness marginal bone becomes visible to the observer on cone-beam computed tomography (CBCT) images and how reconstruction technique and viewing mode affect assessment. Fourteen anterior teeth from six human mandibles were examined with two CBCT resolution protocols: standard- and high-resolution. Distance from the cementoenamel junction to the visible marginal bone level (MBL) was measured in three groups of reconstructed CBCT images: multiplanar reformation (MPR) with grey scale, MPR with inverted grey scale, and 3D rendering. These measurements were used to identify the bone level where marginal bone width should be measured on histological photographs of sliced teeth. Gold standards comprised measurements of bone thickness at the superior MBL on histological photographs. MPR grey scale images exposed at high-resolution settings yielded highest validity: bone widths of 0.173mm (buccal) and 0.356mm (lingual) were necessary for visibility on a CBCT image. 3D-rendered lingual surfaces exposed with high-resolution settings had lowest validity. Intra-observer agreement for all CBCT and histological measurements was high. The best CBCT resolution protocol, reconstruction technique, and viewing mode for analyzing buccal and lingual surfaces of the alveolar bone margin are images exposed with a high-resolution protocol, reconstructed using MPR, and viewed in grey scale. Bone thickness required to be visualized was twice lingually compared to buccally. The visualization of bone thickness in CBCT requires a greater thickness on the lingual side compared to the buccal side. 3D-rendered reconstructions should be avoided when evaluating thin bony structures.

Domain‐independent adaptive histogram‐based features for pomegranate fruit and leaf diseases classification

AbstractDisease identification for fruits and leaves in the field of agriculture is important for estimating production, crop yield, and earnings for farmers. In the specific case of pomegranates, this is challenging because of the wide range of possible diseases and their effects on the plant and the crop. This study presents an adaptive histogram‐based method for solving this problem. Our method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture tasks. The approach explores colour spaces, namely, Red, Green, and Blue along with Grey. The histograms of colour spaces and grey space are analysed based on the notion that as the disease changes, the colour also changes. The proximity between the histograms of grey images with individual colour spaces is estimated to find the closeness of images. Since the grey image is the average of colour spaces (R, G, and B), it can be considered a reference image. For estimating the distance between grey and colour spaces, the proposed approach uses a Chi‐Square distance measure. Further, the method uses an Artificial Neural Network for classification. The effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different diseases. The results show that the method outperforms existing techniques in terms of average classification rate.

End-to-end Facial Recognition Deep Learning Model Specialized for Facial Angle using Gray Image

End-to-end Facial Recognition Deep Learning Model Specialized for Facial Angle using Gray Image

End-to-end Facial Recognition Deep Learning Model Specialized for Facial Angle using Gray Image

End-to-end Facial Recognition Deep Learning Model Specialized for Facial Angle using Gray Image

A Gray Scale Image Compression Using Hierarchical DWT Decomposition and Mixing Quantization Techniques

This paper describes a hybrid grayscale compression system based on the discrete wavelet transform (DWT) and a polynomial coding technique for mixing quantization schemes to increase the compression ratio while maintaining quality. The proposed compression system consists of three main steps: first, decomposing an image using a three-level DWT; second, applying the 2-D linear polynomial coding technique to the approximation sub-band; and third, dividing the detailed sub-bands of each level into the Most Significant Value (MSV) and Least Significant Value (LSV), where the former is compressed using iterative scalar uniform quantization and the latter by soft quantization thresholding. For testing the performance of the suggested compression system, five standard images of size 256×256 pixels were adopted. The suggested technique showed superior performance in terms of reconstructed (decoded) image quality and compression ratio (gain), where the compression ratio is between 21–27 with a PSNR value between 36–38 dB and the compression ratio of JPEG is between 7–20 with a PSNR value between 33–37 dB.

A multi‐path residual network for image denoising based on edge prior information

AbstractEdge‐preserving denoising is important in image analysis. However, most existing methods suffer from smoothing and loss of high frequency detail at the edges. Aiming at this issue, a multi‐path residual network using edge prior information is proposed. Specifically, the edge is first recovered by a newly designed parallel edge extraction module. Then it is used as a priori to generate affine transform parameters to guide the weight distribution between noise and edge, so that the shallow feature extraction pays more attention to preserving edges. In the deep feature extraction stage, a multi‐scale attention unit is designed to provide the spatial neighbourhood information of the feature points to filter and activate the deep features, which further enhances the ability of the network to extract fine‐grained noisy features. Finally, by taking the difference between the noisy image and the noise feature obtained with residual learning, the denoised image is produced. Extensive experiments show that the PSNR is improved by 0.00–0.78 dB on grey image denoising and 0.00–2.69 dB on colour image denoising compared with others. The denoised image has a better ability to preserve image edges, high frequency details and visual perception.

Digital rock modeling of deformed multi-scale media in deep hydrocarbon reservoirs based on in-situ stress-loading CT imaging and U-Net deep learning

Deep and ultra-deep hydrocarbon reservoirs are regarded as an attractive target for onshore oil and gas exploration and development in China. The multi-scale fractures are widely distributed in this type of reservoir. The fluid-solid coupling effect is strong, which can greatly affect the fluid flow, resulting in a low hydrocarbon recovery with the average value less than 15%. To explore the underlying flow dynamics during efficient development of deep and ultra-deep hydrocarbon reservoirs, it is of great importance to characterize the multi-scale fracture-pore structure evolution of rock affected by strong geo-stress field variation. To tackle the above issues, an actual rock drilled from a typical ultra-deep reservoir in Tarim Basin are selected to conduct in-situ stress-loading computed tomography (CT) scanning experiments, and the CT gray images of rock microstructure under different dynamic loading conditions are obtained. A fully convolutional neural network (U-Net) deep learning segmentation algorithm is introduced to accurately distinguish the rock skeleton, pore space and fractures in deep rock. The three-dimensional (3-D) digital rock model of deformed multi-scale fracture-porous media under different dynamic loading conditions is ultimately established to investigate the evolution of fracture morphology and deep rock microstructure as the in-situ stress is gradually loaded. It indicates that, the U-Net deep learning semantic segmentation algorithm can accurately segment CT images of deep rocks, and the established 3D digital rock model of fractured porous media can accurately represent the pore-throat distribution, fracture morphology, and pore-scale topological connectivity. At the initial stage of stress loading, there are few micro-fractures inside the rock, and the fracture connectivity is poor. Due to effect of rock compaction, the average pore throat radius increases while pore throat ratio, coordination number and tortuosity greatly decrease. As the effective stress is gradually loaded, the micro-fractures begin to propagate, leading to stronger heterogeneity of micro-fractures’ distribution and better topological connectivity, and both the coordination number and tortuosity gradually increase. After the deep rock is fractured, micro-fractures run through a fracture network and all the above topological parameters increase greatly.

OFDM Transmission in Rayleigh Fading Channel for S-Box and 3D Chaotic Maps Based Encrypted Image

Data encryption is an important part of the communication system. The Chaos essential properties, make it a crucial candidate for encryption applications. There is a compromise between complexity and security in previous studies. In this study, high security was achieved with low complexity. This paper proposed a 3D chaotic map and S-Box has been cascaded to get a high efficiency as complex algorithms or multi-iteration schemes. The first stage is ciphering using 3D cat-map, the second stage is S-box based on 3D henon map, while the third stage is another ciphering stage using 3D henon map. In this study, various encryption techniques, including cipher algorithms and substitution box, are combined with the OFDM system to establish a secure image transmission over a Rayleigh fading channel. QPSK modulation is used to ensure the simplicity of the proposed system. Six gray images are used, Lena, the cameraman, Barbara, Baboon, pepper and Elaine for testing and comparing with previous works. Security analysis is performed to evaluate the quality and security of the encryption process, the entropy value reach 7.99, correlation coefficient is around zero and the histogram is uniform. In addition, the key size is 2630. For image transmission evaluation, the PSNR and BER are utilized and it reached 10-5 for BER. According to the statistical results, the proposed image encryption scheme is secure and efficient.

Sparse attention with residual pyramidal depthwise separable convolutional based malware detection with optimization mechanism

Recent developments indicate that malware programs present a significant risk in the security and privacy of cloud systems. Existing research in malware detection encounters numerous significant challenges due to the constantly changing and advanced characteristics of malware. Malware detection systems frequently experience high rates of false positives and false negatives, where legitimate applications are incorrectly identified as malware or actual malware remains undetected, which results in operational inefficiencies. Traditional signature-based approaches struggle in recognizing new or modified malware. Additionally, sophisticated malware types, such as file less malware, ransom ware, and rootkits pose detection challenges as they integrate deeply into systems or alter their behaviour to evade detection. These challenges highlight the urgent need for ongoing advancements in this field. Existing methods of malware detection that rely on signatures have been found to be both inefficient and slow in the context of cloud environments. Also, Existing studies have focused on detecting malware by analysing input API calls. But, these models have encountered challenges such as limited accuracy and difficulties in effectively classifying malware types. In contrast, Deep Learning (DL) have shown success by analysing malware behaviour through API calls, which yields encouraging results. Additionally, the data produced by API calls necessitates more computational resources for training. To address these challenges, a new deep learning-based malware detection approach utilizes 2D grayscale images derived from API calls, along with an effective tuning strategy has been proposed. Initially, data are collected from cloud malware dataset. Then, API calls are converted into 2D gray scale images in order to construct gray scale image dataset. After getting the gray scale image, pre-processing is performed to reduce high level noise and to enhance the quality of image by weighted mean filter and anisotropic filter, which helps to improve the performance in classification. Next, these images are then passed into the feature extraction stage to extract sufficient features with an effective integrated densely connected squeeze MobileNet v2 (Ef-DeSMob2), which reduces the dimensionality issue and increase the computational complexity. Then, the collected features are passed into the classification phase to detect normal and malware classes from the samples using sparse attention with residual pyramidal depth wise separable convolutional neural networks (SA:ResPyDSC), which focus to enhance the security and reliability of the model. Finally, the hyper parameters in the classifier model like weights, bias are properly fine-tuned by utilizing hybrid white shark beluga optimization algorithm (Hy-WBeOp). The experimental findings illustrate that the proposed method attains accuracy of 98.06%, precision of 97.99%, recall of 97.05%, f1-score of 96.08% and error metrics like MSE AT 0.08, RMSE of 0.27 and MAE of 0.21, which shows that the proposed method helps to classify the malware classes accurately with less error rates. The proposed approach outperforms with the existing techniques because of its great efficiency. Overall, this approach establish a strong malware detection system classification and enhance the reliability and effectiveness of protection against malicious attacks.

Innovative strategies for intelligent services in smart libraries in the information age based on linear discriminant analysis

With the advent of the information age, to provide better services and ensure the security management of libraries, intelligent facial recognition technology has gradually become a hot research direction in library management. Meanwhile, to further improve the comprehensive performance of facial recognition, this study attempts to integrate principal component analysis and linear discriminant analysis on the basis of analyzing the framework of recognition technology. Afterwards, it introduced support vector machines for recognition and classification, and proposed a new recognition model. The experimental results show that the recognition accuracy of the proposed model is up to 97 % in the ORL dataset and 94 % in the Yale dataset. The recognition error rate is as low as 0.1 % when the number of training samples is 215 and the number of iterations is 200. The model has the best recognition performance when the image size is 25 × 25 mm and the number of noises is 10. In addition, the model is particularly effective in recognition on single person color or gray images, with the highest P-value of 98.7 %, the highest R-value of 98.8 %, and the highest F1-value of 97.5 %. These results show that the proposed model significantly improves the accuracy and robustness of face recognition, and provides strong technical support for intelligent service innovation in smart libraries.

Integrating Kalman filter noise residue into U-Net for robust image denoising: the KU-Net model

In low-level image processing, where the main goal is to reconstruct a clean image from a noise-corrupted version, image denoising continues to be a critical challenge. Although recent developments have led to the introduction of complex architectures to improve denoising performance, these models frequently have more parameters and higher computational demands. Here, we propose a new, simplified architecture called KU-Net, which is intended to achieve better denoising performance while requiring less complexity. KU-Net is an extension of the basic U-Net architecture that incorporates gradient information and noise residue from a Kalman filter. The network’s ability to learn is improved by this deliberate incorporation, which also helps it better preserve minute details in the denoised images. Without using Image augmentation, the proposed model is trained on a limited dataset to show its resilience in restricted training settings. Three essential inputs are processed by the architecture: gradient estimations, the predicted noisy image, and the original noisy grey image. These inputs work together to steer the U-Net’s encoding and decoding stages to generate high-quality denoised outputs. According to our experimental results, KU-Net performs better than traditional models, as demonstrated by its superiority on common metrics like the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). KU-Net notably attains a PSNR of 26.60 dB at a noise level of 50, highlighting its efficacy and potential for more widespread use in image denoising.

A Comparative Study of Mean Square Error, Signal to Noise Ratios of Grey Scale Thresholded and Compressed Images Before Performing Edge Detection Method

Author has already published a paper where he has used colored and grey scale images to find out the mean square error, peak signal to noise ratio’s in clustering based segmented and compressed version. Here in this article author has done the same comparison of image thresolding, image compression and then edge detection techniques respectively. So there will be a sharp comparison between these two different articles where the aim of the author is same for both the cases. In this articles author has used two different types of thresholding techniques: global and adaptive thresholding.

3D reconstruction and morphological characterisation of single wheat grains by X‐ray μCT

SummaryThe intricate task of achieving three‐dimensional (3D) visual reconstruction of wheat kernels represents a notable challenge within the domain of digital grain analysis, playing a pivotal role in the realms of grain storage, processing, and breeding. However, existing investigations focused on individual kernels predominantly encompass dimensions such as length, width, height, and epidermal texture features, with a tendency to be invasive to the internal organisational structure of the kernel. Non‐local mean filtering algorithm is proposed to segment various tissues, and the 2D grey scale images are extracted from X‐ray micro‐computed tomography (μCT) to reconstruct a meticulous 3D visualisation model of individual wheat grains. Furthermore, building upon this foundation, an exhaustive assessment of morphological and structural parameters pertaining to each facet of the internal organisation of the wheat seed grain is conducted. Notably, these calculated parameters align with data generated by prior researchers，with 80% of the volume of the endosperm, 12% of the pericarp, about 2% of the endosperm and scutellum, and 4% of the pores. The established parameters and resultant 3D visual models serve as foundational components for subsequent in‐depth examinations into various physicochemical properties, including quality characteristics, heat, and mass transfer attributes, as well as variations in its morphological structure during breeding, which are pertinent to individual grains. This research contributes valuable insights and methodologies that can propel the advancement of studies in wheat kernel analysis.

Investigation on intermittent flow characteristics in horizontal pipe by visualization measurement method

Accurate measurement and prediction of the intermittent flow characteristics in horizontal pipes is important for constructing multiphase flow models and ensuring pipe flow safety. In this paper, a quantitative image post-processing technique for intermittent flow characteristics based on gray histogram image similarity is proposed, which can realize the measurement of slug frequency. In addition, the technique also has the ability to classify a large number of images, and can quickly find the elongated bubble head, liquid film area and liquid slug area of intermittent flow. On the basis of this technique, combined with image post-processing methods such as gas–liquid interface feature analysis, a set of intermittent flow image processing technique with perfect route is formed. Based on this post-processing technique, the similarity image oscillation trajectories of plug flow and slug flow are obtained. There are differences in the similarity image oscillation trajectories of the two intermittent sub-flow patterns, and the similarity image of the plug flow has an obvious platform period and trailing rising line, which can be used as a basis for the classification of the two intermittent sub-flow patterns. A correlation for predicting the slug frequency of intermittent sub-flow patterns is developed. The accuracy of this slug frequency prediction correlation can be improved by about 10 % compared to not dividing the sub-flow patterns. When the mixture Froude number Frm is less than 5.0, the radial position of the elongated bubble head decreases linearly as the Frm increases. When the Frm is greater than 5.0, the elongated bubble head oscillates near the middle of the pipe. Prediction correlations for the radial position of the elongated bubble head and the slug velocity are established separately, and the maximum error is ± 10 %. The modified mixed Froude number is proposed, and based on this, a new prediction model for the transition from plug flow to slug flow is established.

Linear Codes over $\mathbb{Z}_{p}\mathcal{R}_{1} \mathcal{R}_{2}$ and their Applications

In the paper, we explore the simplex and MacDonald codes over the finite ring $\mathbb{Z}_{p}\mathcal{R}_{1} \mathcal{R}_{2}$. Our investigation focuses on the unique properties of these codes, with the particular attention to their weight distributions and Gray images. The weight distribution is a crucial aspect as it provides insights into the error-detection and error-correction capabilities of the codes. Gray images play a significant role in understanding the structure and behavior of these codes. By examining the dual Gray images of simplex and MacDonald codes over $\mathbb{Z}_{p}\mathcal{R}_{1} \mathcal{R}_{2}$, we aim to develop efficient secret sharing schemes. These schemes benefit from the inherent properties of the codes, such as minimal weight and redundancy, which are essential for secure and reliable information sharing. Understanding the access structure of these schemes is vital, as it determines which subsets of participants can reconstruct the secret. Our study draws on various properties to elucidate this access structure, ensuring that the schemes are secure and efficient. Through this comprehensive analysis, we contribute to the field of coding theory by demonstrating how simplex and MacDonald codes over $\mathbb{Z}_{p}\mathcal{R}_{1} \mathcal{R}_{2}$ can be effectively utilized in cryptographic applications, particularly in designing robust and reliable secret sharing mechanisms.

Conversion of images of 3D friction maps to study the coupling between coefficient of friction, velocity and contact temperature of the disc brake

The coefficient of friction during braking is not constant. Determining its variability requires measurements in real conditions. This paper presents a methodology of conversion of the measured time profiles of the sliding velocity Veq, contact temperature T, and coefficient of friction f, into analytical formulation of two variables z = f(x,y). This was executed in the following steps: 1) creating a three-column table Veq, T, f from experimental measurements; 2) interpolation using Kriging; 3) creating a contour map of the coefficient of friction dependent on the sliding velocity and temperature, 4) import of a grey scale image of the friction map into the finite element (FE) software, and 5) definition of a function z = f(x,y). The studies were carried out for five composite organic friction materials and cast-iron brake disc of a railway vehicle. The developed five friction maps for each friction pair were based the data from the full-scale bench tests during braking from the initial vehicle velocity of 80 kmh−1, 120 kmh−1, 140 kmh−1, 160 kmh−1, 200 kmh−1 to a stop. In the second part of the study the initial-value problem for the equation of motion combined with the heat conduction problem were formulated and solved numerically using special functionalities of the FE software, e.g. Deformed Geometry, Mathematics. Changes in the vehicle velocity and time profiles of the coefficient of friction were determined from the solution of the coupled thermal problem. The computational FE model was verified by comparing the measured and calculated changes in the vehicle velocity, coefficient of friction and evolutions of the brake disc temperature during single braking. The maximum calculated temperature 1 mm under the contact surface of the brake disc, during braking from initial vehicle velocity of 80 kmh−1, was obtained for the base friction material and was equal to T1,3,5Num.= 72 °C. The corresponding experimental value was T1-6Exp.= 66.1 °C. When braking from 140 kmh−1, for the same material it was T1,3,5Num.= 129.7 °C and T1-6Exp.= 130.7 °C. The obtained differences in braking times calculated and measured did not exceed 1.7 %. The corresponding temperature differences of the disc at the stopping time were lower than 5.5 %.

Intelligent mechanical fault diagnosis using multiscale residual network and multisensor fusion

Abstract Mechanical faults in manufacturing systems need to be diagnosed accurately to ensure safety and cost savings. With the development of sensor technologies, data from multiple sensors is frequently utilized to assess the health of intricate industrial systems. In such cases, it is necessary to study the multisensor data based intelligent mechanical fault diagnosis method. First, the multisensor data is converted into grey images and then fused into a three-channel red-green-blue (RGB) image. Then, a multiscale with residual convolution module is proposed, which can extract multiscale deep features of the complex raw signal. Additionally, an attention module for channel and spatial attention is introduced to adaptively adjust the feature response values of each scale. Two datasets and a specific engineering application are used to validate the superiority of the network. The results show that the multisensor multiscale residual network outperforms other fault diagnosis networks in terms of fault identification accuracy, diagnostic efficiency, and applicability.

Research on Polarization Modulation of Electro-Optical Crystals for 3D Imaging Reconstruction.

A method for enhancing the resolution of 3D imaging reconstruction by employing the polarization modulation of electro-optical crystals is proposed. This technique utilizes two polarizers oriented perpendicular to each other along with an electro-optical modulation crystal to achieve high repetition frequency and narrow pulse width gating. By varying the modulation time series of the electro-optical crystal, three-dimensional gray images of the laser at different distances are acquired, and the three-dimensional information of the target is reconstructed using the range energy recovery algorithm. This 3D imaging system can be implemented with large area detectors, independent of the an Intensified Charge-Coupled Device (ICCD) manufacturing process, resulting in improved lateral resolution. Experimental results demonstrate that when imaging a target at the distance of 20 m, the lateral resolution within the region of interest is 2560 × 2160, with a root mean square error of 3.2 cm.

Experimental investigation of junction growth of rough contacts using X-ray computed tomography

The real contact area (RCA) of randomly rough contacts has received a great deal of attention because it correlates strongly with friction, lubrication, sealing, and conductivity. Simulations have revealed that the RCA associated with deterministic normal squeezing loads increases when tangential loads are also applied, in a phenomenon called junction growth. However, experimental investigations of the junction growth of randomly rough contacts are rare. Here, we used X-ray computed tomography (CT) to measure junction growth when two aluminum alloy surfaces were in contact. A high-resolution experimental setup was used to apply loads and observe contact behaviors at a resolution of 4 µm. The RCA and average contact gaps were computed using a three-dimensional (3D) geometric model constructed from gray CT images using the Otsu thresholding method. The results showed that the RCA increased as the normal load increased. The RCA increased by 22.67% after a tangential load was applied (junction growth), and the average gap decreased by 14.01% after a tangential load was applied. Thus, X-ray CT accurately measured the junction growth as a novel quantitative method.

Synergizing a Deep Learning and Enhanced Graph-Partitioning Algorithm for Accurate Individual Rubber Tree-Crown Segmentation from Unmanned Aerial Vehicle Light-Detection and Ranging Data

The precise acquisition of phenotypic parameters for individual trees in plantation forests is important for forest management and resource exploration. The use of Light-Detection and Ranging (LiDAR) technology mounted on Unmanned Aerial Vehicles (UAVs) has become a critical method for forest resource monitoring. Achieving the accurate segmentation of individual tree crowns (ITCs) from UAV LiDAR data remains a significant technical challenge, especially in broad-leaved plantations such as rubber plantations. In this study, we designed an individual tree segmentation framework applicable to dense rubber plantations with complex canopy structures. First, the feature extraction module of PointNet++ was enhanced to precisely extract understory branches. Then, a graph-based segmentation algorithm focusing on the extracted branch and trunk points was designed to segment the point cloud of the rubber plantation. During the segmentation process, a directed acyclic graph is constructed using components generated through grey image clustering in the forest. The edge weights in this graph are determined according to scores calculated using the topologies and heights of the components. Subsequently, ITC segmentation is performed by trimming the edges of the graph to obtain multiple subgraphs representing individual trees. Four different plots were selected to validate the effectiveness of our method, and the widths obtained from our segmented ITCs were compared with the field measurement. As results, the improved PointNet++ achieved an average recall of 94.6% for tree trunk detection, along with an average precision of 96.2%. The accuracy of tree-crown segmentation in the four plots achieved maximal and minimal R2 values of 98.2% and 92.5%, respectively. Further comparative analysis revealed that our method outperforms traditional methods in terms of segmentation accuracy, even in rubber plantations characterized by dense canopies with indistinct boundaries. Thus, our algorithm exhibits great potential for the accurate segmentation of rubber trees, facilitating the acquisition of structural information critical to rubber plantation management.