Weighted Mean Filter Research Articles

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.

Depth Image Rectification Based on an Effective RGB–Depth Boundary Inconsistency Model

Depth image has been widely involved in various tasks of 3D systems with the advancement of depth acquisition sensors in recent years. Depth images suffer from serious distortions near object boundaries due to the limitations of depth sensors or estimation methods. In this paper, a simple method is proposed to rectify the erroneous object boundaries of depth images with the guidance of reference RGB images. First, an RGB–Depth boundary inconsistency model is developed to measure whether collocated pixels in depth and RGB images belong to the same object. The model extracts the structures of RGB and depth images, respectively, by Gaussian functions. The inconsistency of two collocated pixels is then statistically determined inside large-sized local windows. In this way, pixels near object boundaries of depth images are identified to be erroneous when they are inconsistent with collocated ones in RGB images. Second, a depth image rectification method is proposed by embedding the model into a simple weighted mean filter (WMF). Experiment results on two datasets verify that the proposed method well improves the RMSE and SSIM of depth images by 2.556 and 0.028, respectively, compared with recent optimization-based and learning-based methods.

An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images

Medical diagnosis greatly benefits from the use of MRI images. There have been many MRI image enhancement algorithms proposed over the years to aid doctors in disease diagnosis. Conventional image enhancing methods, nevertheless, may also boost the noise that was already there in the captured picture, which might cause distortion, which is unfavourable for the disease diagnosis. Therefore, an appropriate technique for noise suppression is necessary. An attempt has been made to incorporate a two-step filtering based algorithm for noise removal in cardiac MRI images while maintaining the images crucial compositional elements, such as their borders and textures. A combination of adaptive optimum weighted mean filter (AOWMF) and bilateral filter (BF) has been implemented to reduce impulsive noise and Gaussian noise that are both present as mixed noise. The fundamental idea of the AOWMF approach includes the detection of noise pixels using Crow Optimization Algorithm (COA), and replacing them with an optimum weighted mean value depending on a criterion of maximization fitness function. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) have been used to assess the effectiveness of the employed noise reduction technique. The result analysis are experimented in MATLB which reveals that proposed method is able to achieve high PSNR and SSIM and further, this method has the potential to enhance more detail structures of the input cardiac MRI images than existing methods.

Modified U-Net based 3D reconstruction model to estimate volume from multi-view images of a solid object

ABSTRACT This paper proposes a modified U-Net based reconstruction model to automatically estimate the volume of solid objects. Initially, the captured multi-view images are pre-processed using improved adaptive weighted mean filter (IAWMF). Then, the pre-processed image is segmented using modified U-Net based learning mechanism to determine ROI. After segmentation, the key feature points are extracted using Harris corner detection approach. Further, the extracted feature keypoints are matched using Oriented FAST and Rotated BRIEF (ORB) approach and the similarity among two images is calculated using similarity detection algorithm. Then, the multiple image views from various angles are used to calculate the depth map using VGGNet-16 to attain the appropriate volume of the object. Finally, the 3D point clouds are reconstructed from multiple images using dense point cloud generation algorithm for volumetric analysis. Finally, the result shows that the proposed model provides better accuracy with less error than other 3D CAD model.

Modified Weighted Mean Filter to Improve the Baseline Reduction Approach for Emotion Recognition

Participants' emotional reactions are strongly influenced by several factors such as personality traits, intellectual abilities, and gender. Several studies have examined the baseline reduction approach for emotion recognition using electroencephalogram signal patterns containing external and internal interferences, which prevented it from representing participants’ neutral state. Therefore, this study proposes two solutions to overcome this problem. Firstly, it offers a modified weighted mean filter method to eliminate the interference of the electroencephalogram baseline signal. Secondly, it determines an appropriate baseline reduction method to characterize emotional reactions after the smoothing process. Data collected from four scenarios conducted on three datasets was used to reduce the interference and amplitude of the electroencephalogram signals. The result showed that the smoothing process can eliminate interference and lower the signal's amplitude. Based on the three baseline reduction methods, the Relative Difference method is appropriate for characterizing emotional reactions in different electroencephalogram signal patterns and has higher accuracy. Based on testing on the DEAP dataset, these proposed methods achieved accuracies of 97.14, 99.70, and 96.70% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 89.71, 97.63, and 96.58% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 99.59, 98.20, and 99.96% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively.

A Novel Faster RCNN with ODN-Based Rain Removal Technique

During rainy times, the impact of outdoor vision systems gets considerably decreased owing to the visibility barrier, distortion, and blurring instigated by raindrops. So, it is essential to eradicate it from the rainy images for ensuring the reliability of outdoor vision system. To achieve this, several rain removal studies have been performed in recent days. In this view, this paper presents a new Faster Region Convolutional Neural Network (Faster RCNN) with Optimal Densely Connected Networks (DenseNet)-based rain removal technique called FRCNN-ODN. The presented involves weighted mean filtering (WMF) is applied as a denoising technique, which helps to boost the quality of the input image. In addition, Faster RCNN technique is used for rain detection that comprises region proposal network (RPN) and Fast RCNN model. The RPN generates high quality region proposals that are exploited by the Faster RCNN to detect rain drops. Also, the DenseNet model is utilized as a baseline network to generate the feature map. Moreover, sparrow search optimization algorithm (SSOA) is applied to choose the hyperparameters of the DenseNet model namely learning rate, batch size, momentum, and weight decay. An extensive experimental validation process is performed to highlight the effectual outcome of the FRCNN-ODN model and investigated the results with respect to several dimensions. The FRCNN-ODN method produced a higher UIQI of 0.981 for the applied image 1. Furthermore, on the applied image 2, the FRCNN-ODN model achieved a maximum UIQI of 0.982. Furthermore, the FRCNN-ODN algorithm produced a higher UIQI of 0.998 on the applied image 3. The simulation outcome showcased the superior outcome of the FRCNN-ODN (Optimal Densely Connected Networks) model with existing methods in terms of distinct measures.

Research on image to illustration translation method based on CycleGAN

Aiming at the problem that balance between abstract style and original painting content is not enough in traditional image style migration, this paper puts forward an improved method that based on the traditional Cyclic-Consistent Generation Adversarial Networks (CycleGAN), which enables the generator to subsample the feature map of each residual layer, and uses skip link and upsampling to merge the low-level feature and the advanced feature. Then the image averaging operation is used to enhance contrast of the generated images, and weighted mean filter is used to filter out redundant details. Finally, the grey level of the image is reduced in order to improve its abstraction. The experimental results show that with CycleGAN, DualGAN, CartoonGAN compared three methods of migration image style, this model not only improves the image style of abstract degree, also a better retain the original image content, significantly improve the processing and transfer the balance between the content of the original painting abstract style.

Random-Valued Impulse Noise Detection and Removal based on Local Statistics of Images

Random-valued impulse noise removal from images is a challenging task in the field of image processing and computer vision. In this paper, an effective three-step noise removal method was proposed using local statistics of grayscale images. Unlike most existing denoising algorithms that assume the noise density is known, our method estimated the noise density in the first step. Based on the estimated noise density, a noise detector was implemented to detect corrupted pixels in the second step. Finally, a modified weighted mean filter was utilized to restore the detected noisy pixels while leaving the noise-free pixels unchanged. The noise removal performance of our method was compared with 10 well-known denoising algorithms. Experimental results demonstrated that our proposed method outperformed other denoising algorithms in terms of noise detection and image restoration in the vast majority of the cases.

A multi-layer extreme learning machine refined by sparrow search algorithm and weighted mean filter for short-term multi-step wind speed forecasting

With a rapidly growing wind power capacity, wind speed forecasting is of great importance for secure and economical operation of power systems. Nonetheless, due to the volatility of wind speed, its prediction has always been a challenging task. Although utilization of promising noise reduction techniques can mitigate this problem to some extent, wavelet threshold denoising methods (WTDs) suffer from their boundary effects in practice. To address this issue, weighted mean filtering (WMF) is adopted as an alternative of WTDs to reduce the information redundancy of wind speed time series. Furthermore, multi-layer extreme learning machine (MLELM) is combined with WMF to form an ensemble model for one-step and multi-step wind speed forecasting. Besides, as a novel swarm algorithm, the sparrow search algorithm (SSA), is utilized to improve the performance of MLELM. Finally, a data-driven ensemble model WMF-SSA-MLELM is proposed herein, which can be divided into three blocks, data preprocessing, optimization, and prediction. In the first block, WMF suppresses redundant noise in wind speed time series and makes it easier to extract essential features of wind speed. In the second block, SSA optimizes input weights and biases of hidden layers in the optimization stage. In the prediction block, MLELM with two hidden layers optimized by SSA provides the prediction of future wind speed by using denoised wind speed series obtained in the first two steps. The test results on four datasets demonstrate superior accuracy and efficiency of the proposed model WMF-SSA-MLELM, via comparison with all candidate models in both one-step and multi-step wind speed forecasting.

Privacy-preserving inpainting for outsourced image

In this article, a framework of privacy-preserving inpainting for outsourced image and an encrypted-image inpainting scheme are proposed. Different with conventional image inpainting in plaintext domain, there are two entities, that is, content owner and image restorer, in our framework. Content owner first encrypts his or her damaged image for privacy protection and outsources the encrypted, damaged image to image restorer, who may be a cloud server with powerful computation capability. Image restorer performs inpainting in encrypted domain and sends the inpainted and encrypted image back to content owner or authorized receiver, who can acquire final inpainted result in plaintext domain through decryption. In our encrypted-image inpainting scheme, with the assist of Johnson–Lindenstrauss transform that can preserve Euclidean distance between two vectors before and after encryption, the best-matching block with the smallest distance to current block can be found and utilized for patch filling in Paillier-encrypted image. To eliminate mosaic effect after decryption, weighted mean filtering in encrypted domain is conducted with Paillier homomorphic properties. Experimental results show that our privacy-preserving inpainting framework can be effectively applied in secure cloud computing, and the proposed encrypted-image inpainting scheme achieves comparable visual quality of inpainted results with some typical inpainting schemes in plaintext domain.

An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing

In this study, a novel adaptive fuzzy weighted mean filter (AFWMF) model based on the directional median technique and fuzzy inference is presented for solving the restoring high-ratio random-valued noise in image processing. This study aims, not only to obtain information from each direction of the filtering window, but also to gain information from every pixel of the filtering windows completely. Thus, in order to implement preserving details and textures for better restoration in high-noise cases, this study utilizes the directional median to build the membership function in fuzzy inference dynamically, then calculates the weighted window corresponding to the filtering window using fuzzy inference to represent the importance of valuable pixels. Finally, the restoration pixel is calculated using the weighted window and the filtering window for the weighted mean. Subsequently, this new AFWMF model significantly improves performances in the measurement of the peak signal to noise ratio (PSNR) value for preserving detail and fixed image in noise density within the range of 20–70% for the five well-known experimental images. In extensive experiments, this study also shows the better performance of identifying the proposed peak signal-to-removal noise ratio (PSRNR) and evaluating psycho-visual tests than other listed filter methods. Furthermore, the proposed AFWMF model also has a better structural similarity index measure (SSIM) value of another indicator. Conclusively, two interesting and meaning findings are identified: (1) the proposed AFWMF model is generally the best model among the 10 listed filtering methods for image processing in terms of the measurement of two quantitative indicators for both the PSNR and SSIM values; (2) different impulse noise densities should be made for different filtering methods, and thus, this is an important and interesting issue when aiming to identify an appropriate filtering model from a variety of images for processing various noise densities.

Multiscale Random Convolution Broad Learning System for Hyperspectral Image Classification

Deep learning is a powerful technique for image processing. Convolution neural network (CNN) is one of the widely used approaches for hyperspectral image (HSI) classification. These methods mostly need a time-consuming pretraining process to obtain deep features. Random patches networks (RPNets) provide a novel approach that the convolution kernel can be the original image without any pretraining process. In this letter, we propose a novel HSI classification method, multiscale random convolution broad learning system (MRC-BLS), which takes the spatial feature learning by an adaptive weighted mean filter as the convolution kernel to extract local spatial feature in the first layer. Different sizes of random convolution kernels can obtain a multiscale feature map. The weighted fusion of multiscale spatial features extracted by different sizes kernels can get better performance in HSI classification. A broad learning system (BLS) is an efficient classifier to classify images by the multiscale random feature. Experiments in three HSI data sets fully testify to the efficiency and satisfactory performance of the proposed method.

Hyperspectral Image Denoising and Classification Using Multi-Scale Weighted EMAPs and Extreme Learning Machine

Recently, extended multi-attribute profiles (EMAPs) have attracted much attention due to its good performance while applied to remote sensing images feature extraction and classification. Since the EMAPs connect multiple attribute features without considering the pixel-based Hyperspectral Image (HSI) classification, homogeneous regions may become unsmooth due to the noise to be introduced. To tackle this problem, we propose the weighted EMAPs (WEMAPs) to reduce the noise and smoothen the homogeneous regions based on weighted mean filter (WMF). Then, we construct multiscale WEMAPs to product multiscale feature in order to extract different spatial structures of the HSI and produce better classification results. Finally, a new joint decision fusion and feature fusion (JDFFF) framework is proposed based on the decision fusion (DF) and the multiscale WEMAPs (MWEMAPs) based on extreme learning machine (ELM) classifier. That is, the classification results from various scales are combined into a final one with ELM to perform the HSI classification. Experiment results show that the proposed algorithm significantly outperforms many state-of-the-art HSI classification algorithms.

Hyperspectral Image Dimension Reduction and Target Detection Based on Weighted Mean Filter and Manifold Learning

Hyperspectral images have many bands, resulting in a high data volume, which complicates subsequent data processing. Using manifold learning methods to reduce the dimension of data is conducive for subsequent use. However, traditional manifold learning methods are easily disturbed by spectral uncertainty, and are primarily used for hyperspectral image classification. This paper proposes an improved manifold reconstruction preserving embedding algorithm based on weighted mean filter (WMF-IMRPE), that is not easily affected by spectral uncertainty and has excellent target detection performance. The original hyperspectral image is processed by the weighted mean filter to eliminate the influence of noise and reduce spectral differences between the homogeneous ground objects. The spectral angular distance then replaces the Euclidean distance in the original MRPE algorithm to select neighbourhood pixels, reducing spectral uncertainty interference. The experimental results show that the low dimensional features extracted by the WMF-IMRPE algorithm have better distinguishability, and the algorithm further improves the target detection accuracy. The WMF-IMRPE algorithm’s hyperspectral image target detection performance is superior to other similar algorithms.

An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG.

Motor Imagery Electroencephalography (MI-EEG) has shown good prospects in neurorehabilitation, and the entropy-based nonlinear dynamic methods have been successfully applied to feature extraction of MI-EEG. Especially based on Multiscale Fuzzy Entropy (MFE), the fuzzy entropies of the τ coarse-grained sequences in τ scale are calculated and averaged to develop the Composite MFE (CMFE) with more feature information. However, the coarse-grained process fails to match the nonstationary characteristic of MI-EEG by a mean filtering algorithm. In this paper, CMFE is improved by assigning the different weight factors to the different sample points in the coarse-grained process, i.e., using the weighted mean filters instead of the original mean filters, which is conductive to signal filtering and feature extraction, and the resulting personalized Weighted CMFE (WCMFE) is more suitable to represent the nonstationary MI-EEG for different subjects. All the WCMFEs of multi-channel MI-EEG are fused in serial to construct the feature vector, which is evaluated by a back-propagation neural network. Based on a public dataset, extensive experiments are conducted, yielding a relatively higher classification accuracy by WCMFE, and the statistical significance is examined by two-sample t-test. The results suggest that WCMFE is superior to the other entropy-based and traditional feature extraction methods.

Lα Riemannian weighted centers of mass applied to compose an image filter to diffusion tensor imaging

This paper presents an edge preserving and tensor filtering method for diffusion tensor image. The main idea consists in using the Lα Riemannian centers of mass attached to the edge information estimated in the domain of the diffusion tensor so that the image edges not been smoothed in the filtering process. For α ∈ [1, 2], the method encompasses both the standard case of the Riemannian weighted mean filter (α=2) and the Riemannian weighted median filter (α=1) in only one filter. Aiming to establish the fundamentals for the well-posedness of the proposed filter, called adaptive Riemannian filter (ARF), we claimed a theoretical result previously stated in the literature on the continuity of the Lα Riemannian centers of mass, with respect to the parameter α and the points in the neighborhood of the filtered tensor.

A random-valued impulse noise removal algorithm via just noticeable difference threshold detector and weighted variation method

The paper presents a random-valued impulse noise (RVIN) removal algorithm by using distinct impulse noise detection and correction stages. In the noise detection phase, the local luminance adaption of an image is used to detect the obvious noise pixels firstly, and the just noticeable difference (JND) is introduced to detect the noise pixels hiding in image details afterward. In the noise candidate correction phase, the obvious noise pixels, almost distributing in the smooth region, are restored by a weighted mean filter. For the noise candidates hiding in image details, we adopt a weighted detail-preserving variation method to restore them. Since human visual perception and detail-preserving are taken into account during the course of the noises detection and restoration, our method outperforms some existing methods, both in visual and quantitative measurements.

Energy Noise Detection FCM for Breast Tumor Image Segmentation

The traditional fuzzy C@hyphenmeans (FCM) algorithm ignores the spatial information of pixels and is sensitive to noise images. To overcome this disadvantage, this paper proposes a fuzzy C@hyphenmeans clustering algorithm based on energy noise detection (ENDFCM) for mammographic image segmentation. Based on the weighted mean filtering, the algorithm employs the information of the spatial pixel points to enhance its noise resistance. By introducing the energy curve and designing the spatial distance, it constrains the objective function to increase its parameter adaptability. Then, it derives the new subordinate function to increase the relevance of its subordinacy, which is updated continuously during the clustering process. The experimental results show that the algorithm has better segmentation accuracy and robustness compared with the traditional FCM and its improved methods, and can accurately segment the noisy breast tumor images.

Study on an improved-adaptive-weighted mean filtering method for metallographic image processing

Background: Because the noise will bring great error to the analysis of metallographic images an adaptive weighted mean filtering method is proposed to overcome the shortcomings of the standard mean filtering method. Methods: The method is to detect the pulse noise points in the image, and then use the modified mean method to filter out the noise points that are detected. Patents on metallographic image processing have been also discussed for the development of the proposed methodology. Results: It is shown that that filter window can be filtered in comparison with the conventional 3x3, 5x5 and 7x 7 filt window to reduce the noise detection and reduce the complexity of the weight calculation. Conclusion: At the same time, it can better protect the details of the image, has better filtering effect than the standard mean filtering, and its processing speed is faster than the median filtering of the large window, which has profound significance for the edge detection and processing of the metallographic image.

Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-Spectral Manifold Learning.

The graph embedding (GE) methods have been widely applied for dimensionality reduction of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose the proper neighbors for graph construction and explore the spatial information of HSI data. In this paper, we proposed an unsupervised dimensionality reduction algorithm called spatial-spectral manifold reconstruction preserving embedding (SSMRPE) for HSI classification. At first, a weighted mean filter (WMF) is employed to preprocess the image, which aims to reduce the influence of background noise. According to the spatial consistency property of HSI, SSMRPE utilizes a new spatial-spectral combined distance (SSCD) to fuse the spatial structure and spectral information for selecting effective spatial-spectral neighbors of HSI pixels. Then, it explores the spatial relationship between each point and its neighbors to adjust the reconstruction weights to improve the efficiency of manifold reconstruction. As a result, the proposed method can extract the discriminant features and subsequently improve the classification performance of HSI. The experimental results on the PaviaU and Salinas hyperspectral data sets indicate that SSMRPE can achieve better classification results in comparison with some state-of-the-art methods.