Gray Images Research Articles

Improving Average and Median Filters

Average and median filters are the most popular filters used to reduce the negative effects of salt and pepper noise affected gray and color images, these filters work good when the noise affected the image has a small noise ratio. Average and median filters has some disadvantages, which are negatively affect the quality of the denoised image, they treat all the pixels regardless the pixel is a clean pixel or a noisy pixel. In this paper research an improvements will be added to the two filters making them capable to handle salt and pepper noise with high noise ratios. The improvements will be based on using a special mask and special logical index matrices to detect and process only noisy pixels. The improved modified filters will be implemented using noisy images with various noise ratios, the obtained quality factor of the modified filters will be compared with the results of using average and median filters to prove the enhancements provided by the proposed filters.

Digital Image Cryptography using Index Keys

A simple and easy to implement method of data cryptography will be introduced. This method can be easily used to encrypt-decrypt digital images with any type and size, it can be used to treat gray images, color images and secret messages. The simplified encryption and decryption function will minimize the required time for cryptography and they will use a simple operation of pixels’ values rearrangement to apply encryption and decryption. The values rearrangement is to be applied using a generated index key. The index key is generated using the chaotic logistic key formed by running a chaotic logistic map model using a selected values of chaotic parameters. The proposed method will increase the level of image protection by using a private key which is capable to increase the key space to resist hacking attacks. The proposed method will be tested and implemented using various images, the obtained results will be analyzed to prove the quality, efficiency and sensitivity of the proposed method.

Generative Adversarial and Dual Layered Deep Classification Techniques for Improving Block Constructions in Public Cloud

This paper provides the Generative Adversarial and Dual Layered Deep Classification techniques to improve the drawbacks in the methods of Absolute Moment BTC (AMBTC) technique in reconstruction error rate of standard BTC model. The image blocks generation and compression are the main phases of BTC model. This can be applied for both colour images and grey scale images. However, the conventional BTC procedures lacks for edge reconstructions and noise reductions in the output images. The first technique GABTC is developed with multi-layered Deep Neural Network (DNN) structures with GA neural models. The integration of both GA models and BTC principles improve the quality of block constructions and reconstructions significantly. The second proposed work is adopted the Dual layered Deep Classification Technique. Handling the image database with minimal storage complexity, minimal computational complexity and optimal quality is a significant task. To obtain these solutions, many image processing techniques are evolved. In the domain, image compression and decompression are more needed at any cost for effectively handling the complex image databases. E-Learning resources are widely used around the internet based knowledge sharing environments. In the E-Learning environment, multiple types of data resources are managed. Particularly, organizing the images is more crucial task where multiple qualities of images are appeared inside the E-Learning network databases. This problem expects solutions from effective image compression techniques. Block Truncation Coding (BTC) and Absolute Moment BTC (AMBTC) are the techniques provide useful and easy implementations of E-Learning based image compression platform. At the same time, they are limited to image dissimilarity rate. To maintain the quality of images in both compression and decompression phases, multilevel image analysis models and training phases are required. In this regard, this proposed system develops a Dual Layered Deep Classification and Truncation (DLDCT) technique. DLDCT comprises the baseline benefits of BTC, multi-layered Support Vector Machine (SVM) units and Deep Layered Convolutional Neural Network (DLCNN) for producing classified range of image pixels and compressing the images under controlled circumstances. This proposed DLDCT makes the image compression and decompression with determined observations. This reduces real time errors occur during image reconstruction phases. This proposed system has been implemented and compared with existing works with respect to significant performance parameters.

Age and Sex-Related Effects on Single-Subject Gray Matter Networks in Healthy Participants.

Recent developments in image analysis have enabled an individual's brain network to be evaluated and brain age to be predicted from gray matter images. Our study aimed to investigate the effects of age and sex on single-subject gray matter networks using a large sample of healthy participants. We recruited 812 healthy individuals (59.3 ± 14.0 years, 407 females, and 405 males) who underwent three-dimensional T1-weighted magnetic resonance imaging. Similarity-based gray matter networks were constructed, and the following network properties were calculated: normalized clustering, normalized path length, and small-world coefficients. The predicted brain age was computed using a support-vector regression model. We evaluated the network alterations related to age and sex. Additionally, we examined the correlations between the network properties and predicted brain age and compared them with the correlations between the network properties and chronological age. The brain network retained efficient small-world properties regardless of age; however, reduced small-world properties were observed with advancing age. Although women exhibited higher network properties than men and similar age-related network declines as men in the subjects aged < 70 years, faster age-related network declines were observed in women, leading to no differences in sex among the participants aged ≥ 70 years. Brain age correlated well with network properties compared to chronological age in participants aged ≥ 70 years. Although the brain network retained small-world properties, it moved towards randomized networks with aging. Faster age-related network disruptions in women were observed than in men among the elderly. Our findings provide new insights into network alterations underlying aging.

Algorithms for Vision-Based Quality Control of Circularly Symmetric Components.

Quality inspection in the industrial production field is experiencing a strong technological development that benefits from the combination of vision-based techniques with artificial intelligence algorithms. This paper initially addresses the problem of defect identification for circularly symmetric mechanical components, characterized by the presence of periodic elements. In the specific case of knurled washers, we compare the performances of a standard algorithm for the analysis of grey-scale image with a Deep Learning (DL) approach. The standard algorithm is based on the extraction of pseudo-signals derived from the conversion of the grey scale image of concentric annuli. In the DL approach, the component inspection is shifted from the entire sample to specific areas repeated along the object profile where the defect may occur. The standard algorithm provides better results in terms of accuracy and computational time with respect to the DL approach. Nevertheless, DL reaches accuracy higher than 99% when performance is evaluated targeting the identification of damaged teeth. The possibility of extending the methods and the results to other circularly symmetrical components is analyzed and discussed.

A fault diagnosis method based on improved parallel convolutional neural network for rolling bearing

There are many disadvantages for traditional Convolutional Neural Network (CNN) in rolling bearing fault diagnosis, such as low efficiency, weak noise immunity, and poor generalization with changing load. To solve the problem, this paper proposes a methodology of improved parallel CNN (IPCNN). In IPCNN, the simple pooling layer is removed and the parallel structure is to stack directly convolutional layers, with three branches, each branch has 4 layers, where the convolution kernels are all 3 × 3 and the stride sizes are 1, 2, and 3, respectively. The structure that is capable of feature fusion can extract features from the input information. Subsequently, the global average pooling (GAP) layer is used for down sampling, and the bearing faults are classified by the fully connected (FC) layer. In addition, the effectiveness of the proposed model structure is verified by testing the datasets. To further verify the validity of the model, the performance of the model was evaluated by diagnostic accuracy, prediction time, SD, and model size. In order to verify the noise immunity and generalization of the proposed model, the AlexNet, Vgg16, and ResNet18 models are compared, respectively. By performing 2D gray images transformation on the Case Western Reserve University (CWRU) bearing data, rolling bearing fault diagnosis method based on IPCNN model has higher efficiency, stronger noise resistance in the noise environment, and better generalization ability when the load changes.

Efficient Windows malware identification and classification scheme for plant protection information systems.

Due to developments in science and technology, the field of plant protection and the information industry have become increasingly integrated, which has resulted in the creation of plant protection information systems. Plant protection information systems have modernized how pest levels are monitored and improved overall control capabilities. They also provide data to support crop pest monitoring and early warnings and promote the sustainable development of plant protection networks, visualization, and digitization. However, cybercriminals use technologies such as code reuse and automation to generate malware variants, resulting in continuous attacks on plant protection information terminals. Therefore, effective identification of rapidly growing malware and its variants has become critical. Recent studies have shown that malware and its variants can be effectively identified and classified using convolutional neural networks (CNNs) to analyze the similarity between malware binary images. However, the malware images generated by such schemes have the problem of image size imbalance, which affects the accuracy of malware classification. In order to solve the above problems, this paper proposes a malware identification and classification scheme based on bicubic interpolation to improve the security of a plant protection information terminal system. We used the bicubic interpolation algorithm to reconstruct the generated malware images to solve the problem of image size imbalance. We used the Cycle-GAN model for data augmentation to balance the number of samples among malware families and build an efficient malware classification model based on CNNs to improve the malware identification and classification performance of the system. Experimental results show that the system can significantly improve malware classification efficiency. The accuracy of RGB and gray images generated by the Microsoft Malware Classification Challenge Dataset (BIG2015) can reach 99.76% and 99.62%, respectively.

Quantum codes from constacyclic codes over S_{k}

Let S_{k}={mathbb{F}}_{q}[u_{1},u_{2},ldots ,u_{k}]/langle u^{3}_{i}=u_{i},u_{i}u_{j}=u_{j}u_{i}=0 rangle , where 1leq i,jleq k, q=p^{m}, p is an odd prime. First, we define two new Gray maps phi _{k} and varphi _{k}, and study their Gray images. Further, we determine the structure of constacyclic codes and their dual codes, and give a necessary and sufficient conditions of constacyclic codes to contain their duals. Finally, we obtain some new quantum codes over mathbb{F}_{q} by using CSS construction, and compare the constructed codes better than the existing literature.

Simulation and implementation of two-layer oscillatory neural networks for image edge detection: bidirectional and feedforward architectures

The growing number of edge devices in everyday life generates a considerable amount of data that current AI algorithms, like artificial neural networks, cannot handle inside edge devices with limited bandwidth, memory, and energy available. Neuromorphic computing, with low-power oscillatory neural networks (ONNs), is an alternative and attractive solution to solve complex problems at the edge. However, ONN is currently limited with its fully-connected recurrent architecture to solve auto-associative memory problems. In this work, we use an alternative two-layer bidirectional ONN architecture. We introduce a two-layer feedforward ONN architecture to perform image edge detection, using the ONN to replace convolutional filters to scan the image. Using an HNN Matlab emulator and digital ONN design simulations, we report efficient image edge detection from both architectures using various size filters (3 × 3, 5 × 5, and 7 × 7) on black and white images. In contrast, the feedforward architectures can also perform image edge detection on gray scale images. With the digital ONN design, we also assess latency performances and obtain that the bidirectional architecture with a 3 × 3 filter size can perform image edge detection in real-time (camera flow from 25 to 30 images per second) on images with up to 128 × 128 pixels while the feedforward architecture with same 3 × 3 filter size can deal with 170 × 170 pixels, due to its faster computation.

Compare different techniques to retrieve image from database using MATLAB

Newly World Wide Web technologies, there is need to secure data. For securing data cryptography and other relevant techniques were introduced. Multimedia data stream, traditional textual based encryption schemes are not successful so there is need to develop new encryption schemes. During transmission and storage over a network, multimedia content such as digital images and other information are vulnerable to unauthorized access. The images used may be gray or color scale images. There are many measures for testing image quality, like as the mean structural similarity, and mean absolute error, Mean Square Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). These are computed by averaging the squared intensity differences of distorted and original image pixels, along with the related quantity of the PSNR. Results of experiments show that same size images are efficiently encrypted with reliable maximum PSNR as compared with different size images. MATLAB2013a is used for simulation purpose. As refereed by the simulation results, the proposed system not only shows the efficiency in hiding the attributes but also provides better cover image selection.

Transformer High-Voltage Primary Coil Quality Detection Method Based on Machine Vision

Aiming at the problems of low efficiency and low accuracy in manual detection of winding angle and wire spacing during automatic winding of high-voltage primary coils of transmission and distribution transformers, a detection scheme using machine vision is proposed. Firstly, the coil image is acquired by the industrial camera, the detection region is segmented, and the ROI (region of interest) image is pre-processed. For winding angle detection, we propose a slicing method for image graying to reduce the interference caused by uneven light irradiation. The gray image is converted to a binary image, and wire skeleton extraction is performed; the skeleton is identified using the Hough transform for feature straight lines, and the winding angle is then calculated. For wire spacing detection, we propose an intersection of the perpendicular lines method, which extracts edge coordinates using contour images and performs endpoint pixel expansion and shape classification. Use the intersection of the vertical lines to determine the centroid coordinates of the wire outline, calculate the pixel distance of the adjacent centroid, and obtain the wire spacing by combining pixel calibration. Comparison experiments have shown that the solution has a high detection accuracy (0.01 mm), and the error of the integrated detection results is not higher than 10%, which enables the real-time detection of coil winding status and corrects the winding process according to the visual real-time detection result to improve the finished product quality of coils.

Damage identification for steel-concrete composite beams through convolutional neural networks

The identification of damage in the connection of steel–concrete composite beams is pursued by means of Convolutional Neural Networks. The data used for training the networks are gray level images obtained by converting a set of transmissibility functions of the beam. It is shown how simple Convolutional Neural Networks can be trained for the identification of the damage by evaluating its position and intensity. Data availability is thoroughly discussed highlighting the effects of various factors, among them the number and richness of the images used in the training phase, on the predictive capabilities of the network. In this regard, the results obtained are satisfactory as confirmed also by comparisons with data extracted from an experimental campaign. The problem of the unavoidable presence of modelling errors is also discussed and counteracted.

Sparse Depth-Guided Image Enhancement Using Incremental GP with Informative Point Selection.

We propose an online dehazing method with sparse depth priors using an incremental Gaussian Process (iGP). Conventional approaches focus on achieving single image dehazing by using multiple channels. In many robotics platforms, range measurements are directly available, except in a sparse form. This paper exploits direct and possibly sparse depth data in order to achieve efficient and effective dehazing that works for both color and grayscale images. The proposed algorithm is not limited to the channel information and works equally well for both color and gray images. However, efficient depth map estimations (from sparse depth priors) are additionally required. This paper focuses on a highly sparse depth prior for online dehazing. For efficient dehazing, we adopted iGP for incremental depth map estimation and dehazing. Incremental selection of the depth prior was conducted in an information-theoretic way by evaluating mutual information (MI) and other information-based metrics. As per updates, only the most informative depth prior was added, and haze-free images were reconstructed from the atmospheric scattering model with incrementally estimated depth. The proposed method was validated using different scenarios, color images under synthetic fog, real color, and grayscale haze indoors, outdoors, and underwater scenes.

Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion Matrix

In this research, a group of gray texture images of the Brodatz database was studied by building the features database of the images using the gray level co-occurrence matrix (GLCM), where the distance between the pixels was one unit and for four angles (0, 45, 90, 135). The k-means classifier was used to classify the images into a group of classes, starting from two to eight classes, and for all angles used in the co-occurrence matrix. The distribution of the images on the classes was compared by comparing every two methods (projection of one class onto another where the distribution of images was uneven, with one category being the dominant one. The classification results were studied for all cases using the confusion matrix between every Two cases or two steps (two different angles and for the same number of classes). The agreement percentage between the classification results and the various methods was calculated.

Multiscale Analysis for Improving Texture Classification

Information from an image occurs over multiple and distinct spatial scales. Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales. This paper employs the Gaussian–Laplacian pyramid to separately treat different spatial frequency bands of a texture. First, we generate three images corresponding to three levels of the Gaussian–Laplacian pyramid for an input image to capture intrinsic details. Then, we aggregate features extracted from gray and color texture images using bioinspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix feature descriptors, and Haralick statistical feature descriptors into a single feature vector. Such an aggregation aims at producing features that characterize textures to their maximum extent, unlike employing each descriptor separately, which may lose some relevant textural information and reduce the classification performance. The experimental results on texture and histopathologic image datasets have shown the advantages of the proposed method compared to state-of-the-art approaches. Such findings emphasize the importance of multiscale image analysis and corroborate that the descriptors mentioned above are complementary.

FPGA-Based high speed two ways parallel histogram computation for grey image

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

A general family of Plotkin-optimal two-weight codes over $$\mathbb {Z}_4$$

We obtained all possible parameters of Plotkin-optimal two-Lee weight projective codes over \(\mathbb {Z}_4,\) together with their weight distributions. We show the existence of codes with these parameters as well as their weight distributions by constructing an infinite family of two-weight codes. Previously known codes constructed by Shi et al. (Des Codes Cryptogr 88(12): 2493–2505, 2020) can be derived as a special case of our results. We also prove that the Gray image of any Plotkin-optimal two-Lee weight projective codes over \(\mathbb {Z}_4\) has the same parameters and weight distribution as some two-weight binary projective codes of type SU1 in the sense of Calderbank and Kantor (Bull Lond Math Soc 18: 97–122, 1986).

Image Steganography Based on Chaos Function and Randomize Function

The exchange of data is not limited to personal text information or information about institutions and governments, but includes digital media transferred via the Internet including everything, whether texts, images or videos and audio, or animation. These media need high-security protection and high speed during its transmission from one site to another. In this study, a new method is suggested for hiding a gray-level image within a larger color image based on the proposed steganography map that merged chaotic function and randomize function. The size of the chaos and randomize functions is 16 bytes. Experimental results obtained a successful method based on mean squared error, signal-to-noise ratio, peak signal noise rate, embedding capacity, entropy, and histogram. This method can rapidly hide and extract ciphertext in and from the gray image. The original image and the stego image are difficult to distinguish because the correlation between them is very close to 1, indicating that attackers cannot easily differentiate these images with the naked eye. This condition can successfully hide information on the Internet.

Reconstruction of 3D multi-mineral shale digital rock from a 2D image based on multi-point statistics

Introduction: Shale oil and gas reservoirs contain a variety of inorganic and organic pores that differ significantly from conventional reservoirs, making traditional experiments ineffective. Instead, the pore-scale imaging and modeling method, regarded as a novel and practical approach, is proposed to characterize shale microstructure and petrophysical properties. Therefore, it is of great significance to accurately reconstruct the three-dimensional (3D) microstructure of the porous medium, that is, the digital rock. However, microstructural images of shale at high-resolution, obtained through scanning electron microscopy (SEM) are constrained in the two-dimensional (2D) scale.Method: In this work, a novel iterative algorithm to reconstruct 3D multi-phase shale digital rock from a 2D image using multi-point statistics has been proposed. A multi-grid data template was used to capture the conditional probabilities and data events. The novelty of this work stems from an accurate representation of different types of pores and the mineral characteristics of shale rock from 2D images.Result: A series of simulations were conducted to reconstruct 2D shale digital rock from a 2D segmented training image, 3D shale digital rock from a 2D segmented training image, a 2D gray training image to reconstruct 2D shale digital rock, and a 2D gray training image to reconstruct 3D shale digital rock.Discussion: To corroborate the accuracy of the reconstructed digital rock and evaluate the reliability of the proposed algorithm, we compared the construction image with the training image with the two-point correlation function, geometry, morphological topology structure, and flow characteristics. The reconstruction accuracy indicates that the proposed algorithm can replicate the higher-order statistical information of the training image.

Spacecraft depth completion based on the gray image and the sparse depth map

Perceiving the three-dimensional (3D) structure of the spacecraft is a prerequisite for successfully executing many on-orbit space missions, and it can provide critical input for many downstream vision algorithms. In this paper, we propose to sense the 3D structure of spacecraft using light detection and ranging sensor (LIDAR) and a monocular camera. To this end, Spacecraft Depth Completion Network (SDCNet) is proposed to recover the dense depth map based on gray image and sparse depth map. Specifically, SDCNet decomposes the object-level spacecraft depth completion task into foreground segmentation subtask and foreground depth completion subtask, which segments the spacecraft region first and then performs depth completion on the segmented foreground area. In this way, the background interference to foreground spacecraft depth completion is effectively avoided. Moreover, an attention-based feature fusion module is also proposed to aggregate the complementary information between different inputs, which deduces the correlation between different features along the channel and the spatial dimension sequentially. Besides, four metrics are also proposed to evaluate object-level depth completion performance, which can more intuitively reflect the quality of spacecraft depth completion results. Finally, a large-scale satellite depth completion dataset is constructed for training and testing spacecraft depth completion algorithms. Empirical experiments on the dataset demonstrate the effectiveness of the proposed SDCNet, which achieves 0.225m mean absolute error of interest and 0.778m mean absolute truncation error, surpassing state-of-the-art methods by a large margin. The spacecraft pose estimation experiment is also conducted based on the depth completion results, and the experimental results indicate that the predicted dense depth map could meet the needs of downstream vision tasks.