Laplacian Filter Research Articles

Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering.

Clustering plays a crucial role in analyzing scRNA-seq data and has been widely used in studying cellular distribution over the past few years. However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular perspective. To address these challenges, we propose a novel approach, called multi-level multi-view network based on structural consistency contrastive learning (scMMN), for scRNA-seq data clustering. Firstly, the proposed method constructs shallow views through the $k$-nearest neighbor ($k$NN) and diffusion mapping (DM) algorithms, and then deep views are generated by utilizing the graph Laplacian filters. These deep multi-view data serve as the input for representation learning. To improve the clustering performance of scRNA-seq data, contrastive learning is introduced to enhance the discrimination ability of our network. Specifically, we construct a group contrastive loss for representation features and a structural consistency contrastive loss for structural relationships. Extensive experiments on eight real scRNA-seq datasets show that the proposed method outperforms other state-of-the-art methods in scRNA-seq data clustering tasks. Our source code has already been available at https://github.com/szq0816/scMMN.

Optimizing pulmonary carcinoma detection through image segmentation using evolutionary algorithms

<span lang="EN-US">This paper’s goal is to suggest an image segmentation technique for use with medical images, specifically computer tomography scan images, to aid doctors in understanding the images. To address a variety of picture segmentation issues, it is necessary to investigate and apply novel evolutionary algorithms. The study focuses on pulmonary carcinoma, which is the cancer that affects males the most frequently across the globe. For proper treatment and life-saving measures, early identification of lung cancer is essential. To identify lung cancer, doctors frequently employ the computed tomography imaging technique. In order to extract tumours from lung scans, the study analyses the effectiveness of three optimization algorithms: k-means clustering, particle swarm optimization, and modified guaranteed convergence particle swarm optimization. The study also examines the pre-processing performance of four filters, namely the mean, bilateral, gaussian, and laplacian filters, shows that the bilateral filter is best suited for CT scans of the body. To test the proposed technique on 30 examples of lung scans. The proposed algorithm is tested on 30 sample lung images. The results show that the modified guaranteed convergence particle swarm optimization algorithm has the highest accuracy of 96.01%.</span>

Saliency-based video summarization for face anti-spoofing

With the growing availability of databases for face presentation attack detection, researchers are increasingly focusing on video-based face anti-spoofing methods that involve hundreds to thousands of images for training the models. However, there is currently no clear consensus on the optimal number of frames in a video to improve face spoofing detection. Inspired by the visual saliency theory, we present a video summarization method for face anti-spoofing detection that aims to enhance the performance and efficiency of deep learning models by leveraging visual saliency. In particular, saliency information is extracted from the differences between the Laplacian and Wiener filter outputs of the source images, enabling the identification of the most visually salient regions within each frame. Subsequently, the source images are decomposed into base and detail images, enhancing the representation of the most important information. Weighting maps are then computed based on the saliency information, indicating the importance of each pixel in the image. By linearly combining the base and detail images using the weighting maps, the method fuses the source images to create a single representative image that summarizes the entire video. The key contribution of the proposed method lies in demonstrating how visual saliency can be used as a data-centric approach to improve the performance and efficiency for face presentation attack detection. By focusing on the most salient images or regions within the images, a more representative and diverse training set can be created, potentially leading to more effective models. To validate the method’s effectiveness, a simple CNN–RNN deep learning architecture was used, and the experimental results showcased state-of-the-art performance on four challenging face anti-spoofing datasets.

Evaluation on different volume of fluid methods in unstructured solver under the optimized condition

We compared the accuracy of volume of fluid (VOF) methods in unstructured solvers using the following five different methods: 1 - the algebraically compressive VOF method, 2 – simple coupled VOF method with Level Set (S-CLSVOF) method, 3 - interface-compressing VOF method incorporated with Laplacian filter (VOFL), 4 - isoAdvector method, and 5 - isoAdvector method incorporated with Laplacian filter (isoAdvectorL) by incorporating them into OpenFOAM®, an open-source software. To evaluate these methods under proper conditions, we compared the calculation accuracy using the optimized parameters, which are explored by Bayesian optimization. The test cases for advection accuracy of volume fraction and for imbalance of surface tension force in static multiphase fluid fields were considered. In this study, we found that the compression parameters and maximum Courant number should be adjusted to obtain high accuracy simulation according to the simulation condition in VOF and S-CLSVOF method. In VOFL and isoAdvectorL methods, the spurious current can be extremely reduced, which means that these methods are suitable for slow flow with higher Laplace number conditions.

Digital Image Noise Reduction Based on Proposed Smoothing and Sharpening Filters

In order to reduce noise in digital photographs, this paper provides a thorough analysis of sophisticated sharpness enhancement and image smoothing approaches. The research compares and contrasts different approaches, including well-known filters like Gaussian filter, Mean filter, Weighted Averaging filter, Laplacian filters, and Unsharp mask, with suggested strategies aimed at achieving optimal efficiency. The performance of these techniques is evaluated in several circumstances with speckle noise and Gaussian noise corrupted images. The results show how well the suggested strategy performs in terms of reducing noise, preserving image features, and improving overall image quality. The comparative advantage of the suggested technique over conventional filters is highlighted by comparative analysis. The study's conclusion offers information on the possible uses and future developments of these methods in practical image processing situations.

Mineralogy and rare earth elements spatial distribution in the carboniferous rocks of the eastern El Galala El Bahariya, Egypt

The Carboniferous rocks in the eastern part of El Galala El Bahariya were investigated using integrated field and laboratory techniques to determine their mineralogy, rare earth elements (REEs) distribution, and structural lineament density. The succession is mainly composed of argillaceous and minor arenaceous rocks, exhibiting variations in lithology across the study area. The essential minerals of these rocks are quartz, kaolinite, and illite. The accessory minerals include microcline, gypsum, anhydrite, halite, barite, hematite, pyrite, anatase and gibbsite, in addition to, the radioactive and REEs-bearing minerals such as uranophane, xenotime, monazite, and zircon. These minerals are reported in the rocks of the study area for the first time. The types, forms, habits, and modes of occurrence of the recorded minerals indicate multiple origins: allogenic–authigenic, and primary-secondary. REEs in the rocks exhibit enriched patterns with a negative Eu anomaly, likely due to low plagioclase content or and/or Eu removal by alteration processes. The distribution of REEs is influenced by textural attributes, with finer sediments in the southern part showing higher REEs content, ascribed to the high clay content and presence of gibbsite. The preferential mobility of LREEs is evident, explaining elevated LREEs/HREEs ratios in the rocks. Utilizing remote sensing techniques, lithological units and alteration zones were determined using decorrelation stretch and band ratio methods. The structural features, identified by Laplacian filter and edge enhancement, revealed the presence of NW–SE, N–S, and NE–SW faults that structurally regulate alteration zones and REEs distribution. These alteration zones are associated with clay minerals, REEs concentrations, and high lineament structure density. Spatial distribution maps highlight higher REEs concentrations in the southern part of the study area. These findings were validated using various analytical methods, including mineralogical and geochemical investigations, main component analysis, minimum noise fraction, decorrelation stretch, and spectral reflectance studies. They provide new insights into the REEs potential of the Carboniferous rocks and heighten our understanding of REEs genesis and distribution in the region.

Computational image analysis of distortion, sharpness, and depth of field in a next-generation hybrid exoscopic and microsurgical operative platform.

The development of surgical microscope-associated cameras has given rise to a new operating style embodied by hybrid microsurgical and exoscopic operative systems. These platforms utilize specialized camera systems to visualize cranial neuroanatomy at various depths. Our study aims to understand how different camera settings in a novel hybrid exoscope system influence image quality in the context of neurosurgical procedures. We built an image database using captured cadaveric dissection images obtained with a prototype version of a hybrid (microsurgical/exoscopic) operative platform. We performed comprehensive 4K-resolution image capture using 76 camera settings across three magnification levels and two working distances. Computer algorithms such as structural similarity (SSIM) and mean squared error (MSE) were used to measure image distortion across different camera settings. We utilized a Laplacian filter to compute the overall sharpness of the acquired images. Additionally, a monocular depth estimation deep learning model was used to examine the image's capability to visualize the depth of deeper structures accurately. A total of 1,368 high-resolution pictures were captured. The SSIM index ranged from 0.63 to 0.85. The MSE was nearly zero for all image batches. It was determined that the exoscope could accurately detect both the sharpness and depth based on the Laplacian filter and depth maps, respectively. Our findings demonstrate that users can utilize the full range of camera settings available on the exoscope, including adjustments to aperture, color saturation, contrast, sharpness, and brilliance, without introducing significant image distortions relative to the standard mode. The evolution of the camera incorporated into a surgical microscope enables exoscopic visualization during cranial base surgery. Our result should encourage surgeons to take full advantage of the exoscope's extensive range of camera settings to match their personal preferences or specific clinical requirements of the surgical scenario. This places the exoscope as an invaluable asset in contemporary surgical practice, merging high-definition imaging with ergonomic design and adaptable operability.

A novel parallel mammogram sharpening framework using modified Laplacian filter for lumps identification on GPU

A novel parallel mammogram sharpening framework using modified Laplacian filter for lumps identification on GPU

Quality Control of Doppler Spectra from a Vertically Pointing, S-Band Profiling Radar

Abstract This study describes a novel combination of methods to remove spurious spectral peaks, or “spurs,” from Doppler spectra produced by a vertically pointing, S-band radar. The University of Massachusetts S-band frequency-modulated, continuous-wave radar (UMass FMCW) was deployed to monitor the growth of the CBL over northern Alabama during the VORTEX–Southeast field campaign in 2016. The Doppler spectra contained spurs caused by high-voltage switching power supplies in the traveling wave tube amplifier. In the original data-processing scheme for this radar, a median filtering method was used to eliminate most of the spurs, but the largest ones persisted, which significantly degraded the quality of derived radar moments (e.g., reflectivity, Doppler velocity, and spectrum width) and hindered further analysis of these data (e.g., hydrometeor classification and boundary layer height tracking). Our technique for removing the spurs consists of three steps: (i) a Laplacian filter identifies and masks peaks in the spectra that are characteristic of the spurs in shape and amplitude, (ii) an in-painting method then fills in the masked area based on surrounding data, and (iii) the moments data (e.g., reflectivity, Doppler velocity, and spectrum width) are then recomputed using a coherent power technique. This combination of techniques was more effective than the median filter at removing the largest spurs from the Doppler spectra and preserved more of the underlying Doppler spectral structure of the scatterers. Performance of both the median-filter and the in-painting methods is assessed through statistical analysis of the spectral power differences. Downstream products, such as boundary layer height detection, are more easily derived from the recomputed moments. Significance Statement This manuscript describes a novel combination of image and signal processing techniques used to recover meteorological observations from corrupted Doppler radar spectra. This successful recovery of meteorologically significant information illustrates the importance of retaining Doppler spectra when practical. In seeking solutions to data quality issues, the atmospheric science community should remain cognizant of promising techniques offered by other disciplines. We present this data rescue study as an example to the meteorological community.

An adaptive anisotropic bilateral filtering method for mesh data in scale space

Three-dimensional mesh data of parts, such as blades and engine bodies, have been widely used in industrial fields. Due to the different kinds of noise during mesh acquisition and the machining deficiency of parts, the mesh quality tends to be insufficient for subsequent operations. Therefore, mesh denoising is a necessary and critical procedure to improve mesh quality. Existing methods commonly apply geometry features smoothing, which may also create unexpected results, such as volume shrinkage and blurring of sharp edges. This paper proposed an adaptive anisotropic bilateral filtering method for mesh data in scale space. Firstly, the mesh is decomposed into a smooth base with low frequency and a height vector field with high frequency based on scale space theory. The denoising of the vertex spatial field is transformed into the denoising of the height vector field, aiming to only consider high-frequency information. Secondly, the bilateral filter scheme with the anisotropic Gaussian kernel is proposed to denoise the height vector field, removing noise mixed with features. The parameters in the bilateral filter scheme are chosen adaptively by maximizing the designed probability density function. The mean square angular error of the proposed method is less than 0.15 rad, which is superior to the general-purpose algorithms, for instance, Laplacian filtering, bilateral mesh filtering and bilateral normal filtering algorithm.

Consistency of the inverse scattering imaging condition, the energy norm imaging condition and the impedance kernel in acoustic and elastic reverse-time migration

Abstract In this work, we draw connections between the imaging conditions using the impedance kernel, the inverse scattering imaging condition, and the energy norm imaging condition in acoustic and elastic reverse-time migration (RTM). Traditional RTM often introduces large low-wavenumber artifacts that degrade image quality in intricate geological structures with large velocity variations. In practice, the Laplacian filter is commonly used to remove these low-wavenumber artifacts, but it changes the image wavenumber spectrum. The advanced imaging conditions of the inverse scattering, the energy norm, and the impedance kernel can effectively remove the low-wavenumber artifacts while not changing the wavenumber spectrum. This study aims to build a connection between these three types of imaging conditions by conducting detailed analysis in the wavenumber domain for acoustic and elastic RTMs. We find that they are exactly the same except for the varying weights of the source-receiver wavefield cross-correlation. All three imaging conditions can generate clear RTM images that are not affected by low-wavenumber artifacts. Numerical examples for a simple model, Sigsbee 2a, and BP models verify the consistency of these three imaging conditions and show their advantage over conventional simple zero-lag cross-correlation imaging conditions. This is important for improving the quality and reliability of seismic imaging technology.

Classification of benign and malignant solid breast lesions on the ultrasound images based on the textural features: the importance of the perifocal lesion area

The amount of ultrasound (US) breast exams continues to grow because of the wider endorsement of breast cancer screening programs. When a solid lesion is found during the US the primary task is to decide if it requires a biopsy. Therefore, our goal was to develop a noninvasive US grayscale image analysis for benign and malignant solid breast lesion differentiation. We used a dataset consisting of 105 ultrasound images with 50 benign and 55 malignant non-cystic lesions. Features were extracted from the source image, the image of the gradient module after applying the Sobel filter, and the image after the Laplace filter. Subsequently, eight gray-level co-occurrence matrices (GLCM) were constructed for each lesion, and 13 Haralick textural features were calculated for each GLCM. Additionally, we computed the differences in feature values at different spatial shifts and the differences in feature values between the inner and outer areas of the lesion. The LASSO method was employed to determine the most significant features for classification. Finally, the lesion classification was carried out by various methods. The use of LASSO regression for feature selection enabled us to identify the most significant features for classification. Out of the 13 features selected by the LASSO method, four described the perilesional tissue, two represented the inner area of the lesion and five described the image of the gradient module. The final model achieved a sensitivity of 98%, specificity of 96%, and accuracy of 97%. Considering the perilesional area, Haralick feature differences, and the image of the gradient module can provide crucial parameters for accurate classification of US images. Features with a low AUC index (less than 0.6 in our case) can also be important for improving the quality of classification.

Deep video inpainting detection and localization based on ConvNeXt dual-stream network

Currently, deep learning-based video inpainting algorithms can fill in a specified video region with visually plausible content, usually leaving imperceptible traces. Since deep video inpainting methods can be used to maliciously manipulate video content, there is an urgent need for an effective method to detect and localize deep video inpainting. In this paper, we propose a dual-stream video inpainting detection network, which includes a ConvNeXt dual-stream encoder and a multi-scale feature cross-fusion decoder. To further explore the spatial and temporal traces left by deep inpainting, we extract motion residuals and enhance them using 3D convolution and SRM filtering. Furthermore, we extract filtered residuals using LoG and Laplacian filtering. These residuals are then entered into ConvNeXt, thereby learning discriminative inpainting features. To enhance detection accuracy, we design a top-down pyramid decoder that aims at deep fusion of multi-dimensional multi-scale features to fully exploit the information of different dimensions and levels in detail. We created two datasets containing state-of-the-art video inpainting algorithms and conducted various experiments to evaluate our approach. The experimental results demonstrate that our approach outperforms existing methods and attains a competitive performance despite encountering unseen inpainting algorithms.

Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods

Mesh filtering of surfaces is crucial for noise reduction, feature preservation, and mesh simplification in graphics, visualization, and computer vision. In this paper, the detail preservation capacities of 3 frequently used filters, i.e., Bilateral, Laplacian, and Taubin mesh filters, in mesh filtering have been thoroughly examined by experiments conducted on 4 different test meshes. While the Bilateral filter excels in preserving sharp features due to its integration of geometric proximity with intensity similarity, the Laplacian filter prioritizes smoothness by averaging neighboring vertex positions, and the Taubin filter offers a balanced approach by merging attributes of both Laplacian and high-pass filters. The Bilateral filter's primary strength lies in its ability to maintain sharp features on a mesh, ensuring that intricate details are preserved by considering both the spatial closeness and intensity similarity of vertices. The Laplacian filter, although effective in achieving mesh smoothness, has the propensity to excessively smooth out sharp and defining features, potentially causing a loss of critical details in the processed mesh. The Taubin filter integrates the best of both worlds, ensuring smoothness without excessive mesh shrinkage; however, it might not excel in feature preservation as effectively as the Bilateral filter or smooth as uniformly as the Laplacian filter, making it a middle-ground option for certain applications. The statistical analysis of the experimental results has shown that the Taubin method is statistically a more successful mesh filtering method for the test sets used in this paper.

Dense Convolution Neural Network for Automated Diabetic Retinopathy Detection

Diabetic Retinopathy is a diabetes complication due to uncontrolled levels of glucose in the body which leads to abnormality in the eyes which owing to distortion in the retina and leading to permanent damage of the eye or vision losses. Hence identification and classification of diabetic retinopathy through manual observation is a challenging process due to composite boundaries and features with a high degree of intraclass variation and a low degree of interclass variation.
 Unsupervised machine learning algorithms have been used to classify diseases automatically based on the appearance of lesions and their characteristics. These models require more processing time and less reliability. It has been proposed that deep learning architecture can overcome these limitations as it is more efficient and accurate at detecting the lesion’s features.
 The novel dense convolution neural network has been proposed with preprocessing, segmentation, feature extraction and classification steps. Laplacian filter and CLAHE techniques have been used in a preprocessing step. The region growing algorithm, Principal Component Analysis and Dense CNN have been used for segmentation, Feature extraction and classification of DR lesions. Furthermore, the proposed model was compared with conventional classifiers in terms of Accuracy parameters. The proposed model achieves 97.88 % of Accuracy. It improves computing efficiency and minimizes network complexity. Hence the proposed model can accurately detect the lesions in the retinal fundus images.

Texture features analysis technique to detect mass lesion in digitized mammogram images

Mass lesions are one of the breast cancer tumors. Mammogram images are the first screening tool to detect tumors in the women breast, but due to radiologist fatigue, number of false positive (FP) and false negative (FN) rates are increased. The main objective of this paper is to develop an intelligent computer aided diagnosis (CAD) system that can accurately detect mass lesions in digitized mammogram images. The proposed method has three stages. The first stage is a preprocessing stage, where the mass lesion is enhanced using a customized Laplacian filter. Then, multi-statistical filters are implemented to detect a potential mass lesion in the mammogram images. In the final stage, the number detected FP regions are reduced using five texture features. The proposed algorithm is evaluated using 45 mammogram images and the algorithm achieved an accuracy rate of 97% in detecting mass lesion with 83% sensitivity rate and 98% specificity rate.

Enhancement of Product-Inspection Accuracy Using Convolutional Neural Network and Laplacian Filter to Automate Industrial Manufacturing Processes

The automation of the manufacturing process of printed circuit boards (PCBs) requires accurate PCB inspections, which in turn require clear images that accurately represent the product PCBs. However, if low-quality images are captured during the involved image-capturing process, accurate PCB inspections cannot be guaranteed. Therefore, this study proposes a method to effectively detect defective images for PCB inspection. This method involves using a convolutional neural network (CNN) and a Laplacian filter to achieve a higher accuracy of the classification of the obtained images as normal and defective images than that obtained using existing methods, with the results showing an improvement of 11.87%. Notably, the classification accuracy obtained using both a CNN and Laplacian filter is higher than that obtained using only CNNs. Furthermore, applying the proposed method to images of computer components other than PCBs results in a 5.2% increase in classification accuracy compared with only using CNNs.

U-Net-based gannet sine cosine algorithm enabled lesion segmentation and deep CNN for diabetic retinopathy classification

ABSTRACT Diabetic retinopathy (DR) is a reason for blindness. This disease raises a threat of developing deteriorations in blood vessels supplied by the retina. In this research, U-Net_Gannet Sine Cosine Algorithm (U-Net_GSCA) is used for DR segmentation, and DR classification is made by the GSCA_Deep Convolutional Neural Network (GSCA_DeepCNN). In the pre-processing process the Region of Interest (ROI) is extracted. For pre-processing process, it uses Laplacian filter. Color spacing, rotation, and brightness are the augmentation techniques used for image augmentation. U-Net which is tuned by GSCA is used for lesion segmentation. Furthermore, GSCA is the integration of GOA with SCA. The segmentation of the artery and vein is performed utilising the sparking process. In the feature extraction phase, they uses augmented and segmented images. From this images, the feature extraction is made. Finally, the classification of DR is made by the deep CNN that is also trained by GSCA. Furthermore, U-Net_GSCA achieved 92.6% segmentation accuracy, GSCA_DeepCNN achieved 92.2% classification accuracy, 92.5% sensitivity, and 91.9% specificity.

Performance evaluation of linear and nonlinear filters for despeckling B mode foetal heart ultrasound images

ABSTRACT Early detection of congenital heart disease (CHD), one of the most commonly occurring congenital defects, is important to reduce mortality rates. The major drawback of ultrasound imaging is the inherent speckle noise, making visual examination of anatomical structures a challenging task. This study discusses the effect of denoising using different linear and nonlinear filters on B mode foetal cardiac ultrasound images. The exhaustive study of the performance of linear filters such as mean, Laplacian, and Wiener, and nonlinear filters such as median, anisotropic diffusion, non-local means, and bilateral filters has been carried out. Performance of the filtering technique is evaluated using various fidelity measures and a new assessment parameter edge preserving index (EPI) is also used for evaluation. Non-local means filter outperforms other filtering techniques with average fidelity values as follows: PSNR: 67.97 dB, MSE: 0.01283, RMSE: 0.109, average difference (AD): 0.005, normalized absolute error (NAE): 2 × 10−4, mean absolute error (MAE): 0.011, EPI: 0.999. This work serves as a reference on pre-processing techniques that can be applied to foetal cardiac ultrasound images for beginners.

High-speed recursive-separable image processing filters with variable scanning aperture sizes

In the process of development of computer technologies, the number of areas of their application naturally grows and, along with it, the complexity of the tasks to be solved, which entails the need for new research. Similar tasks include digital filtering of images in the field of medical technologies and active-pulse television measuring systems. There are many methods and algorithms of digital filtering designed to solve the problem of improving the quality; algorithms that can improve the quality of images while reducing computational costs are widely used. High demands, which are made due to the constant growth in the size of the generated images, as well as the requirement for modern television systems, is real-time operation. When solving practical problems, it is required to use different filter aperture sizes, which provide an increase in quality and preservation of image details. The solution of these problems was the reason for the emergence of adaptive filters that are able to change the parameters in the process of processing the received data, while not spending additional time on processing with an increase in the size of the aperture. The paper presents the principles of constructing adaptive image processing filters, which, by obtaining an input parameter indicating the required dimension of a multi-element aperture, are able to implement the construction of the required aperture. The Laplacian “Truncated Pyramid” filter and the “double pyramid” Laplacian were modified. A feature of these filters is the oddness of the multi-element aperture, so the coefficient used to build the mask is always set to odd. When using these filters, it is possible to use two coefficients that are responsible for increasing the filtration efficiency, since, in their original form, the Laplacian filters have a sum of coefficients equal to zero. The experiment shows a comparison with high-dimensional filters that work when using classical two-dimensional convolution. The next stage of the presented research will be the application of parallel computing techniques, which will increase the speed of the developed filters.