Smoothing Filter Research Articles

Computational fluid dynamics in carbonate rock wormholes using magnetic resonance images as structural information

Computational fluid dynamics (CFD) is an essential tool with growing applications in many fields. In petrophysics, it is common to use computed tomography in those simulations, but in medicine, magnetic resonance imaging (MRI) is also being used as a basis for structural information. Wormholes are high-permeability structures created by the acidification of carbonate reservoirs and can impact reservoir production. CFD combined with MRI can benefit the study of wormholes in petrophysics, but combining both techniques is still a challenge. The objective of this study is to develop a pipeline for performing CFD in wormholes with MRI data. Using three samples of carbonate rocks acidified with 1.5% hydrochloric acid at 0.1, 1, and 10 ml/min, we acquired 300μm resolution T2-weighted images and experimental measurements of pressure data within flow rates of 5 to 50 ml/min. We applied cropping, bias field correction, non-local means denoising, and segmentation in the image processing step. For the 3D reconstruction, we used marching cubes to generate the surface mesh, the Taubin filter for surface smoothing, and boundary modeling with Blender. Finally, for the CFD, we generated volumetric meshes with cfMesh and used the OpenFOAM simpleFoam solver to simulate an incompressible, stationary, and laminar flow. We analyzed the effect of surface smoothing, estimating edge displacements, and measured the simulation pressure at the same flow rates as the experiments. Surface smoothing had a negligible impact on the overall edge position. For most flow rates, the simulation and experimental pressure measurements matched. A possible reason for the discrepancies is that we did not consider the surrounding porous media in the simulations. In summary, our work had satisfactory results, demonstrating CFD’s feasibility in studying wormholes using MRI.

Refining disparity maps using deep learning and edge-aware smoothing filter

Stereo matching algorithm is crucial for applications that rely on three-dimensional (3D) surface reconstruction, producing a disparity map that contains depth information by computing the disparity values between corresponding points from a stereo image pair. In order to yield desirable results, the proposed stereo matching algorithm must possess a high degree of resilience against radiometric variation and edge inconsistencies. In this article convolutional neural network (CNN) is employed in the first stage to generate the raw matching cost, which is subsequently filtered with a bilateral filter (BF) and applied with cross-based cost aggregation (CBCA) during the cost aggregation stage to enhance precision. Winner-take-all (WTA) strategy is implemented to normalise the disparity map values. Finally, the resulting output is subjected to an edge-aware smoothing filter (EASF) to reduce the noise. Due to its resistance to high contrast and brightness, the filter is found to be effective in refining and eliminating noise from the output image. Despite discontinuities like adiron's lost cup handle or artl's shattered rods, this approach, based on experimental research utilizing a Middlebury standard validation benchmark, yields a high level of accuracy, with an average non-occluded error of 6.79%, comparable to other published methods.

Single-shot enhanced imaging through multimode fiber based on self-learning networks

High-fidelity imaging through a multimode fiber (MMF) plays a crucial role in various fields such as medicine, communication, and detection. However, the optical transmission matrix of the MMF suffers from dimensionality reduction. This case results in poor reconstruction quality such as low resolution and noise. In this paper, an unsupervised self-learning circulate learning network is employed to enhance a single degraded image without ground truth images. Moreover, an edge-preserving smoothing filter is applied to address the heavy noise problem of the reconstructed images. Experimental results demonstrate that the proposed method can improve the dimensionality and fidelity of the reconstructed target. Compared to traditional transmission matrix-based reconstruction methods, we have a competitive advantage in terms of evaluation metrics. The proposed method further advances the development of imaging through a multimode fiber.

A NEW METHOD FOR MEDICAL IMAGE FUSION BASED ON GAUSSIAN BLUR FILTER AND ROBINSON COMPASS OPERATOR

Medical image fusion is a process of extracting features from multi-modal medical images and combining them into a composite image. It brings huge support in medical imaging and clinical diagnosis. However, the extraction of both structural and functional information from input MRI and PET images using multi-scale transform fusion methods poses a challenge of providing high-quality decomposition layers since during the decomposition process, images can still lose information such as blur or noise at the edges of the image. To address this limitation, we present a new method to improve the visual information fidelity of medical image fusion. Firstly, the YCbCr color space is utilized to prevent distortion when merging color and grey images. The second algorithm uses the CLAHE model, which allows the input images to have good contrast. Then, a Gaussian blur filter is employed to decompose the images into base and detail layers. The use of Gaussian blur ensures a smoothing filter of the edges. After that, the Robinson compass operator is applied to create the fusion rule of detail components. Finally, the fused base and detail layers are concatenated together to form the final composite image. The experimental results show that the proposed approach outperforms the latest methods in bringing visual information fidelity of the input images to the fused image, which is helpful in supporting doctors and radiologists in visual analysis of the medical images.

Frequency-modulated continuous-wave laser interferometry measurement method based on cross-correlation

Frequency-modulated continuous-wave (FMCW) laser interferometry technology holds significant potential for applications in the fields of ultraprecision manufacturing and high-precision sensing. This paper proposes a novel approach among current phase demodulation methods is based on cross-correlation to address the challenge of this technology. On the basis of nonlinear correction of a distributed feedback laser, the intercepted beat frequency signal was first preprocessed with Z-score signal normalization and a smoothing filter. Subsequently, the interference beat signal was subjected to processing using a correlation method to derive the correlation function. Finally, the phase difference between adjacent beat signals was determined by pinpointing the maximum value of the cross-correlation function, enabling accurate displacement demodulation. Experimental validation was performed by constructing an FMCW laser interferometric displacement measurement system. The results indicated that the standard deviation of the displacement error for the cross-correlation method was 2.41 nm during static measurements. Compared to conventional maximum-point method, the static measurement error of the cross-correlation method has been reduced by 1.43 times. In dynamic measurements in the 500 μm range, The measurement error of the cross-correlation method has been reduced by 6.04 times, avoiding the dynamic measurement positioning problem of conventional feature point demodulation methods and making the measurement results more accurate. This advancement holds substantial practical value in the realm of phase demodulation in laser interferometry.

A complexity efficient penta-diagonal quantum smoothing filter for bio-medical signal denoising: a study on ECG

Extracting information-bearing signal from a noisy environment has been a practical challenge in both classical and quantum computing formalism, especially in critical signal processing applications. To filter out the effect of noise, we propose a quantum smoothing filter built upon quantum formalism-based circuits applied for electrocardiogram signal denoising. The proposed quantum filter is a conceptually novel framework with an advantage in computational complexity as compared to the existing classical filters, such as discrete wavelet transform and empirical mode decomposition, whereas it achieves similar performance metrics for the accuracy of the filter. Further, we exploit the penta-diagonal Toeplitz structure of the smoothing filter, which gives approximately 48%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$48\\%$$\\end{document} gate cost reduction for 10 qubit circuit compared to the standard Hamiltonian simulation without structure. The run-time complexity using the quantum matrix inversion technique for the structured matrix is given by O~κ2poly(logN)εP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ilde{{\\mathscr {O}}}\\left( \\frac{\\kappa ^2 \ ext {poly}(\\log {N})}{\\varepsilon _P}\\right)$$\\end{document} for condition number κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document} of the N×N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\ imes N$$\\end{document} filter matrix within precision εP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon _P$$\\end{document}. Embedding fixed sparsity of the banded matrix, the quantum filter shows potentially better run-time complexity than classical filtering techniques. For the quantifiable research results of our work, we have shown several performance metrics, such as mean-square error and peak signal-to-noise ratio analysis, with a bound of error due to observation noise, simulation error and quantum measurement uncertainty.

An integrated SDN framework for early detection of DDoS attacks in cloud computing

Cloud computing is a rapidly advancing technology with numerous benefits, such as increased availability, scalability, and flexibility. Relocating computing infrastructure to a network simplifies hardware and software resource monitoring in the cloud. Software-Defined Networking (SDN)-based cloud networking improves cloud infrastructure efficiency by dynamically allocating and utilizing network resources. While SDN cloud networks offer numerous advantages, they are vulnerable to Distributed Denial-of-Service (DDoS) attacks. DDoS attacks try to stop genuine users from using services and drain network resources to reduce performance or shut down services. However, early-stage detection of DDoS attack patterns in cloud environments remains challenging. Current methods detect DDoS at the SDN controller level, which is often time-consuming. We recommend focusing on SDN switches for early detection. Due to the large volume of data from diverse sources, we recommend traffic clustering and traffic anomalies prediction which is of DDoS attacks at each switch. Furthermore, to consolidate the data from multiple clusters, event correlation is performed to understand network behavior and detect coordinated attack activities. Many existing techniques stay behind for early detection and integration of multiple techniques to detect DDoS attack patterns. In this paper, we introduce a more efficient and effectively integrated SDN framework that addresses a gap in previous DDoS solutions. Our framework enables early and accurate detection of DDoS traffic patterns within SDN-based cloud environments. In this framework, we use Recursive Feature Elimination (RFE), Density Based Spatial Clustering (DBSCAN), time series techniques like Auto Regressive Integrated Moving Average (ARIMA), Lyapunov exponent, exponential smoothing filter, dynamic threshold, and lastly, Rule-based classifier. We have evaluated the proposed RDAER model on the CICDDoS 2019 dataset, that achieved an accuracy level of 99.92% and a fast detection time of 20 s, outperforming existing methods.

Model-based impending lithium battery terminal voltage collapse detection via data-driven and machine learning approaches

Lithium battery is an important power source of an electrical vehicle (EV). Practically, when a battery is about to fail, it needs to be removed from the load because keeping it connected can lead to permanent damage. Hence, it is important to detect battery failure to sustain the lifespan of the battery and avoid safety issues which will contribute to the sustainability of the EV. However, determining the effective disconnecting or discharging moment of the battery remains a challenging issue in EV applications. In order to solve the abovementioned problem, a control framework and maximum likelihood estimation are proposed to estimate the parameters of a battery model. In addition, both the sparse representation and dynamic mode decomposition (DMD) approaches are applied to protect the physical battery unit from a failure scenario. For comparison purposes, the proposed framework is compared to a Lyapunov-based detection approach along with neural network (NN) and linear discriminant analysis (LDA). Moreover, several state estimation algorithms are applied to estimate battery state of charge (SOC) at each time step: extended Kalman filter, extended Kalman smooth variable structure filter, and cubature Kalman filter. Finally, the experimental results showed that the proposed DMD approach outperforms the sparse representation, NN, LDA, and Lyapunov-based approaches in terms of detection accuracy. Moreover, the proposed DMD strategy is more robust towards the inaccuracy of the estimated SOC. Besides that, the proposed battery model parameter estimation approach exhibited fast processing time; therefore, it is a reliable choice for recomputing the model parameters from time to time to compensate aging effect. An autonomous unmanned aerial vehicle (UAV) was simulated as a proof-of-concept in a MATLAB environment to verify the detection performance of the proposed methods. Both actual and UAV results showed that the DMD demonstrated more robust detection performance than other methods in terms of processing time and detection accuracy.

Hyperspectral Image Classification Using Improved Active Deep Learning Framework Including Edge Preserving Filters

To extract valuable information from satellite data for applications such as agriculture, geological research, and environmental monitoring, the classification of hyperspectral images is an essential task. Labeling each pixel in this process is time-consuming and requires financial resources. To this end, working with a small number of samples is very important. In order to provide high classification performances with a limited number of samples, this paper aims to enhance the performance with an active learning framework. The framework incorporates dimensionality reduction, an edge-preserving filter, and active learning steps. From this perspective, we investigated different edge-preserving filter methods to analyze the effects on performance. By combining edge-preserving filters with dimensionality reduction, the study presents a unique method that improves classification performance while maintaining image quality and reducing noise. The following five edge-preserving smoothing filters are evaluated: weighted least squares (WLS), Joint-Histogram weighted median filter (Joint WMF), fast global image smoother (FGS), bilateral filter (BF), and static/dynamic (SD). Our experiments demonstrate that compared to the reference research (CNN+AL+MRF), the proposed framework increased overall and average accuracies about 2-5% for Indian Pines, Pavia University, and Salinas datasets.

Impact of enhancement filters of a CMOS system on halo artifact expression at the bone-to-implant interface.

To assess the impact of enhancement filters on the formation of halo artifacts in radiographs of dental implants obtained with a complementary metal oxide semiconductor (CMOS) system. Digital radiographs of dental implants placed in dry human mandibles were processed with the Noise Reduction smoothing filter, as well as the Sharpen 1, Sharpen 4, and Sharpen UM high-pass filters available in the CLINIVIEW™ software (Instrumentarium Dental, Tuusula, Finland). Subjective analysis involved evaluating the left, right, and apical surfaces of each implant for the presence of much, few, or no halo. The objective analysis involved measurement of the halo area using the Trainable Weka Segmentation plugin (ImageJ, National Institutes of Health, Bethesda, MD, USA). Data were analyzed using Friedman's test (subjective analysis) and ANOVA (objective analysis) (α = 5%). In the subjective evaluation, the Sharpen 4 filter produced more radiographs with much halo present, and in the objective evaluation, a bigger halo area when compared to the original images and the Noise Reduction filter for all surfaces (p < 0.05). When evaluating dental implants, priority should be given to original images and those enhanced with smoothing filters since they exhibit fewer halo artifacts. Post-processing tools, such as enhancement filters, may improve the image quality and assist some diagnostic tasks. However, little is known regarding the impact of enhancement filters in halo formation on CMOS systems, which have been increasingly used in dental offices.

Dual-Band Image Fusion Approach Using Regional Weight Analysis Combined with a Multi-Level Smoothing Filter

Image fusion is an effective and efficient way to express the feature information of an infrared image and abundant detailed information of a visible image in a single fused image. However, obtaining a fused result with good visual effect, while preserving and inheriting those characteristic details, seems a challenging problem. In this paper, by combining a multi-level smoothing filter and regional weight analysis, a dual-band image fusion approach is proposed. Firstly, a series of dual-band image layers with different details are obtained using smoothing results. With different parameters in a bilateral filter, different smoothed results are achieved at different levels. Secondly, regional weight maps are generated for each image layer, and then we fuse the dual-band image layers with their corresponding regional weight map. Finally, by imposing proper weights, those fused image layers are synthetized. Through comparison with seven excellent fusion methods, both subjective and objective evaluations for the experimental results indicate that the proposed approach can produce the best fused image, which has the best visual effect with good contrast, and those small details are preserved and highlighted, too. In particular, for the image pairs with a size of 640 × 480, the algorithm could provide a good visual effect result within 2.86 s, and the result has almost the best objective metrics.

Spectral Total-variation Processing of Shapes—Theory and Applications

We present a comprehensive analysis of total variation (TV) on non-Euclidean domains and its eigenfunctions. We specifically address parameterized surfaces, a natural representation of the shapes used in 3D graphics. Our work sheds new light on the celebrated Beltrami and Anisotropic TV flows and explains experimental findings from recent years on shape spectral TV [Fumero et al. 2020 ] and adaptive anisotropic spectral TV [Biton and Gilboa 2022 ]. A new notion of convexity on surfaces is derived by characterizing structures that are stable throughout the TV flow, performed on surfaces. We establish and numerically demonstrate quantitative relationships between TV, area, eigenvalue, and eigenfunctions of the TV operator on surfaces. Moreover, we expand the shape spectral TV toolkit to include zero-homogeneous flows, leading to efficient and versatile shape processing methods. These methods are exemplified through applications in smoothing, enhancement, and exaggeration filters. We introduce a novel method that, for the first time, addresses the shape deformation task using TV. This deformation technique is characterized by the concentration of deformation along geometrical bottlenecks, shown to coincide with the discontinuities of eigenfunctions. Overall, our findings elucidate recent experimental observations in spectral TV, provide a diverse framework for shape filtering, and present the first TV-based approach to shape deformation.

A new smooth variable structure Tobit filter for systems with censored measurements and model parameters uncertainty

The smooth variable structure filter (SVSF) is an effective state estimation strategy for systems with model parameters uncertainty. The superiority of SVSF, however, depends on accurate sensor measurements. In practice, the accuracy of traditional SVSF algorithm will be degraded or even diverge for censored measurements. To solve this problem, in this paper, firstly, the Tobit censored model is introduced to describe censored phenomenon for systems with model parameters uncertainty and censored measurements. The innovation, residual, innovation covariance and state prediction error cross-covariance are revised based on cumulative probability density. Then, the near optimal smooth boundary layer (SBL) is recalculated based on the minimum trace principle of estimation error covariance. A robust filter gain is redesigned based on the convergence of estimation error absolute, and the necessary conditions for the convergence of the proposed filter are proved. In the end, oscillators, distributed target tracking and the Udacity self-driving data-set are used to verify that the proposed algorithm has higher accuracy and better robustness under model parameters uncertainty and censored measurements.

CT image restoration method via total variation and L 0 smoothing filter

Abstract In X-ray CT imaging, there are some cases where the obtained CT images have serious ring artifacts and noise, and these degraded CT images seriously affect the quality of clinical diagnosis. Thus, developing an effective method that can simultaneously suppress ring artifacts and noise is of great importance. Total variation (TV) is a famous prior regularization for image denoising in the image processing field, however, for degraded CT images, it can suppress the noise but fail to reduce the ring artifacts. To address this issue, the L 0 L_{0} smoothing filter is incorporated with TV prior for CT ring artifacts and noise removal problem where the problem is transformed into several optimization sub-problems which are iteratively solved. The experiments demonstrate that the ring artifacts and noise presented in the CT image can be effectively suppressed by the proposed method and meanwhile the detailed features such as edge structure can be well preserved. As the superiority of TV and L 0 L_{0} smoothing filters are fully utilized, the performance of the proposed method is better than the existing methods such as the TV-based method and L 0 L_{0} -based method.

Window size analysis in ultrasonic temperature estimation with timeshifts

In this work, ultrasonic timeshift method for temperature change estimation was investigated for 2D simulated in-silico synthetic ultrasonic signals. Digital phantom tissue was created in MATLAB environment and acoustic simulation was running on k-Wave toolbox for two different temperature conditions. First temperature distribution was assigned to tissue as uniform 37 °C. Second temperature distribution is Gaussian form with peak at tissue center as 45 °C and tails of Gaussian curve is 37 °C. Signal was analyzed with ultrasonic timeshift method for temperature change estimation. This method is based on four steps, calibration with tissue constant, finding timeshift with cross correlation algorithm, find slope of timeshift vector respect to timestep, and multiply tissue constant and slope of local timeshift vector. This multiplication gives temperature change of local point. In this work, window size of smoothing filter of timeshift vector and linear fitting to timeshift—timestep data was analyzed as parametrically with range 3λ to 10λ with a 1λ increment for both windows equally. As a result, window parameters as 5λ to 7λ give best results, maximum absolute error is 0.82 °C, 0.97 °C and 0.92 °C respectively and mean absolute error is ∼0.35 °C. As a verify, different analysis was performed on different temperature distribution with discrete two peak curves.

Cascaded Thinning in Upscale and Downscale Representation for EEG Signal Processing.

Smoothing filters are widely used in EEG signal processing for noise removal while preserving signals' features. Inspired by our recent work on Upscale and Downscale Representation (UDR), this paper proposes a cascade arrangement of some effective image-processing techniques for signal filtering in the image domain. The UDR concept is to visualize EEG signals at an appropriate line width and convert it to a binary image. The smoothing process is then conducted by skeletonizing the signal object to a unit width and projecting it back to the time domain. Two successive UDRs could result in a better-smoothing performance, but their binary image conversion should be restricted. The process is computationally ineffective, especially at higher line width values. Cascaded Thinning UDR (CTUDR) is proposed, exploiting morphological operations to perform a two-stage upscale and downscale within one binary image representation. CTUDR is verified on a signal smoothing and classification task and compared with conventional techniques, such as the Moving Average, the Binomial, the Median, and the Savitzky Golay filters. Simulated EEG data with added white Gaussian noise is employed in the former, while cognitive conflict data obtained from a 3D object selection task is utilized in the latter. CTUDR outperforms its counterparts, scoring the best fitting error and correlation coefficient in signal smoothing while achieving the highest gain in Accuracy (0.7640%) and F-measure (0.7607%) when used as a smoothing filter for training data of EEGNet.

Artificial Intelligence-Assisted Evaluation of English Teaching Effectiveness in Colleges and Universities

Abstract Education informatization is an important initiative for the country to realize education modernization, which is related to the development of China’s education. The use of a single evaluation of teaching effectiveness obviously cannot meet the assessment needs. Based on the premise of intelligent assistance through a hybrid brain-computer interface, this paper aims to study an evaluation method for teaching effectiveness using artificial intelligence. Firstly, EEG signals are collected through an EEG acquisition device, and then they are preprocessed. Second, the pre-processed signals are subjected to feature extraction, and then the features are classified using a specific pattern recognition method, and finally the classification results are used to assess the effectiveness of English teaching for college students. Based on the evaluation results, the learning system is optimized and improved. The signals collected by EEG devices carry a lot of noise, and in this paper, the processing of the original signals includes a smoothing filter, de-baseline filter, filter filter, and Kalman filter to achieve a higher signal-to-noise ratio. Multivariate evaluation results are provided by the evaluation system that is assisted by artificial intelligence. Compared with other evaluation methods to enrich the evaluation information, based on the above system effectiveness evaluation study, as far as the F1 value is concerned, this paper’s method is the highest, reaching 0.815, which is 0.048 higher than the highest F1 value of the traditional algorithm, and in the other two values, this paper’s method is also higher than other methods. This verifies the rationality and effectiveness of the teaching effectiveness evaluation system based on a hybrid brain-computer interface proposed in this paper.

Цифровой сглаживающий трапецеидальный рекурсивно-сепарабельный фильтр обработки изображений с изменяемыми размерами сканирующей многоэлементной апертуры

The development of television systems is an important factor for many industries involved in the acquisition, processing, storage and transmission of images. Today, an urgent task in the use of such systems is to improve the quality of images obtained using digital photo and video cameras. To solve this problem, digital recursive-separable smoothing filters can be used. The paper describes the process of operation of the algorithm for changing the size of the scanning multi-element aperture of a smoothing trapezoidal recursive-separable filter for digital image processing. The results of evaluating its performance relative to the same algorithm implemented through classical two-dimensional convolution for various sizes of test images are presented. The influence of the aperture size of the developed filter on the change in the signal-to-noise ratio is assessed. The algorithm is implemented in the MATLAB computing environment.

Trilateral filter smoothing and denoising algorithm for gated light ranging and imaging

Trilateral filter smoothing and denoising algorithm for gated light ranging and imaging

Photometric Stereo Method Used for Woven Fabric Density Measurement Based on 3D Surface Structure

The measurement of the density of woven fabrics based on the vision method has been widely developed. This study used a photometric stereo method to measure the warp and weft density of woven fabrics based on the 3D surface structure. Six 2D images of the fabric were recorded, each with a different lighting direction. The six images were then reconstructed using the unbiased photometric stereo algorithm to produce the three-dimensional surface structure. The reconstructed image was used to detect and correct the skew angle with the Hough transform. For each image, a depth profile was made toward the x-axis to get the weft curve and towards the y-axis to get the warped curve. The two depth curves were filtered using a locally weighted smoothing (LOESS) filter. This study successfully measured the density of woven fabric with an average error for warp and weft of 0.64% and 0.45%, respectively.