Multiresolution Technique Research Articles

A new method for enhancing signatures of ocean surface waves in nautical X-band radar images

Nautical X-band radars are used to continuously monitor large ocean surface areas with high temporal and spatial resolution. The signatures of ocean surface waves in nautical X-band radar images are typically present as straight or curved stripes with distinct directional and frequency characteristics. Curvelet transform (CT) is a multi-resolution technique with the capability of locational and directional resolving. This study presents a new method for enhancing the signatures of ocean surface waves in nautical X-band radar images. A radar image is decomposed at different scales, directions, and locations using forward CT. The surface wave signals are distributed in the curvelet coefficient (CC) of certain directions and scales. The signals of surface waves are extracted by retaining the CCs in specific directions and scales, whereas the CCs in other directions and scales are all set to zero. Wave signatures are enhanced by adding the extracted signals back to the original image. The proposed method is also feasible for enhancing signatures of surface waves in synthetic aperture radar or optical remote sensing images.

Effects of source space resolution, randomization and averaging in focal EEG source localization: Estimation of forward and inverse errors

In this article, we study randomized multiresolution forward modeling and source localization in electroencephalography (EEG). Our goal is to determine numerically the effects of variable source space resolution on both forward modeling and source localization perspectives and to examine how it affects the detection of the sources at different depths inside the brain. Our concentration is in the main principles of multiresolution methods: random source space generation and the reconstruction obtained via averaging over multiple maximum a posteriori (MAP) estimations obtained at a finite set of different sub source spaces, and their suggested enhancement in distinguishability of weak components. The motivation of averaging is to reduce the discretization and optimization error and marginalize other variable factors. Due to the unguided randomized source space generation, multiple samples of the same size source spaces are needed. For this very reason, a large number of source spaces of multiple source counts are used to obtain statistically feasible results in this paper. This study verifies the described postulates of the multiresolution technique by showing averaging to be crucial in order to obtain a robust projection to a lower resolution, where the lead field matrix is overdetermined. From the viewpoint of source localization, the multiresolution approach was found to improve the reconstruction accuracy obtained with each resolution level below the highest and observed to be necessary to achieve an accuracy comparable to that of forward modeling.

Development of GPU-Paralleled multi-resolution techniques for Lagrangian-based CFD code in nuclear thermal-hydraulics and safety

In this study, we propose a fully parallelized adaptive particle refinement (APR) algorithm for smoothed particle hydrodynamics (SPH) to construct a stable and efficient multi-resolution computing system for nuclear safety analysis. The APR technique, widely employed by SPH research groups to adjust local particle resolutions, currently operates on a serialized algorithm. However, this serialized approach diminishes the computational efficiency of the system, negating the advantages of acceleration achieved through high-performance computing devices. To address this drawback, we propose a fully parallelized APR algorithm designed to enhance both efficiency and computational accuracy, facilitated by a new adaptive smoothing length model. For model validation, we simulated both hydrostatic and hydrodynamic benchmark cases in 2D and 3D environments. The results demonstrate improved computational efficiency compared to the conventional SPH method and APR with a serialized algorithm, and the model's accuracy was confirmed, revealing favorable outcomes near the resolution interface. Through the analysis of jet breakup, we verified the performance and accuracy of the model, emphasizing its applicability in practical nuclear safety analysis.

Semantic and Context Understanding for Sentiment Analysis in Hindi Handwritten Character Recognition Using a Multiresolution Technique

The rapid growth of Web 2.0, which enables people to generate, communicate, and share information, has resulted in an increase in the total number of users. In developing countries, online users’ sentiment influences decision-making, social views, individual consumption decisions, and entity quality monitoring. As a result, more accurate sentiment analysis, particularly in their native language such as Hindi, is preferred over crude binary categorization. This is because of the abundance of web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. Analyzing this data and recovering valuable and relevant information from handwritten text has become extremely important. Despite years of research and development, no optical writing recognition (OCR) system has ever been certified as completely reliable. The first step in any pattern recognition system is feature selection. In many fields, feature selection is studied as a combinatorial optimization problem. The primary goal of feature selection is to reduce the number of redundant and ineffective traits in the recognition system. This feature selection is used to maintain or improve the performance of the classifier used by the recognition system: A support vector machine (SVM) technique could be used to solve this character recognition problem. The Hindi character recognition system recognizes Hindi characters by employing morphological operations, edge detection, HOG feature extraction, and an SVM-based classifier. The proposed model outperformed the current state-of-the-art method, achieving an accuracy of 96.77%.

Some compactly supported Riesz wavelets associated to any Ed(2)(ℤ) dilation

Let [Formula: see text] be a [Formula: see text] dilation matrix with integer entries and with determinant [Formula: see text]. The existence of Riesz wavelets with compact support associated to [Formula: see text] is proved. Our proof is constructive and the generators of these Riesz wavelets may be taken to be symmetric, with high regularity and many vanishing moments. In our construction, we first study the structure of the quotient group [Formula: see text]. Afterwards and perhaps the main advance in this paper is the fact that we obtain a family of trigonometric polynomials on [Formula: see text] with zeros only in [Formula: see text]. At this point, we are able to get scaling functions with compact support of a multiresolution analysis. In addition, Riesz wavelets with compact support hold by standard multiresolution techniques. Finally, we give some examples of Riesz wavelets where we emphasize on a numerical estimation of their regularity.

Co-Movement of Digital Currencies and Asian Stock Indexes: A Wavelets Based Multiresolution Analysis

The paper examines the co-movement relationship between Cryptocurrency and Asian major indices through Wavelet Coherence multiresolution technique and the Granger causality for result robustness. The indices data of Singapore and Japan stock market was used from January 2018 to December 2022, the outcome of our study shows great of co-relationship in the movement between Asian two major listed equity market indices and Cryptocurrencies investments. Moreover, the study shows that Japan stock market found to be more positively correlated with existing cryptocurrencies in comparison to Singapore stock market.

High Sound-Contrast Inverse Scattering by MR-MF-DBIM Scheme

In ultrasound tomography, cross-sectional images represent the spatial distribution of the physical parameters of a target of interest, which can be obtained based on scattered ultrasound measurements. These measurements can be obtained from dense datasets collected at different transmitter and receiver locations, and using multiple frequencies. The Born approximation method, which provides a simple linear relationship between the objective function and the scattering field, has been adopted to resolve the inverse scattering problem. The distorted Born iterative method (DBIM), which utilizes the first-order Born approximation, is a productive diffraction tomography scheme. In this article, the range of interpolation applications is extended at the multilayer level, taking into account the advantages of integrating this multilayer level with multiple frequencies for the DBIM. Specifically, we consider: (a) a multi-resolution technique, i.e., a multi-step interpolation for the DBIM: MR-DBIM, with the advantage that the normalized absolute error is reduced by 18.67% and 37.21% in comparison with one-step interpolation DBIM and typical DBIM, respectively; (b) the integration of multi-resolution and multi-frequency techniques with the DBIM: MR-MF-DBIM, which is applied to image targets with high sound contrast in a strongly scattering medium. Relative to MR-DBIM, this integration offers a 44.01% reduction in the normalized absolute error.

A new approach of image denoising based on adaptive multi-resolution technique

Medical imaging and diagnostic techniques have become popular over the last two decades with the advancement of data science, data analysis, data storage, and the internet. The impact of this evolution can be seen in the fields of telemedicine and medical sciences, which allow more effective detection and treatment of various diseases. Like any other form of imaging technique, medical images are sensitive to noise and artifacts. The images become unclear with the presence of noise, and the diseases cannot be identified properly. Therefore, image denoising plays a vital role in the field of biomedical image processing. As a result, work must be done to minimize noise without sacrificing image quality. Various methods for reducing noise have already been proposed in the literature. Each method has its own set of benefits and drawbacks. In this paper, we introduce a bi-dimensional empirical mode decomposition (BEMD)-based image de-noising approach. The principal purpose of this research is to decompose noisy images depending on frequency and create a hybrid algorithm that incorporates existing de-noising approaches. The proposed algorithm is an image-dependent technique that decomposes the noisy image into several IMFs with residue, then considering the individual attributes of the IMFs, they are separately filtered. Furthermore equalization is applied to the residue for preserving the edge information. A comprehensive study is conducted over the experimental results of the benchmark test images using different performance measure matrices to quantify the effectiveness of the presented approach. In terms of subjective and objective evaluation, the reconstructed image is found to be more accurate and visually pleasing. It also outperforms the state-of-the-art image-denoising methods, especially in terms of PSNR, RMSE, correlation, and structural similarity.

A novel multi-resolution technique for solving complex vorticity patterns in planar viscous flows past bodies through the DVH method

The Diffused Vortex Hydrodynamics (DVH) is a Vortex Particle Method widely validated in the last decade. This numerical approach allows cost-effective simulations of viscous flows past bodies at moderate and high Reynolds numbers, by taking into account only the rotational part of the flow field. In the present work a novel multi-resolution technique is presented in order to limit the number of particles in the computational domain, further improving the solver efficiency. The proposed technique preserves the total circulation and uses the (Benson et al., 1989) deterministic algorithm to regularize the particle spatial distribution during the diffusion step. Simulations of the planar flow past five cylindrical sections at Re=10,000 are discussed: flat plate, triangle, square, circular cylinder and a symmetrical airfoil. Although the simulations are carried out in a two-dimensional framework, complex vorticity patterns develop because of the flow separation and the Reynolds number considered. Comparisons with a Finite Volume solver are carried out and discussed, such highlighting the advantages of the present numerical approach.

Border mapping multi-resolution (BMMR) technique for incompressible projection-based particle methods

A novel multi-resolution technique called border mapping multi-resolution (BMMR) is proposed for projection-based particle methods. The BMMR aims to obtain background equivalent particle distributions in the two sides of a border between sub-domains with a 2:1 resolution ratio so that a single resolution framework is adopted to near-border particles calculations. The novelty of the BMMR is that it obtains the background grid from the mapping of the actual particle distribution in the border and, as a result, the location of the particles in the background grid exactly matches the location of the actual particles. In this way, such technique aims to reduce the error from the interpolation of the physical quantities in the background grid and to avoid sudden changes in the particle distribution that may lead to unstable local pressure calculation. In the coarse side of the border, the refinement is made by a triangulation and by placing fictitious particles in the midpoints of the triangles. In the fine side of the border, the derefinement is made by defining a set of fine particles that result in a particle distribution that best resembles a coarse one. The accuracy and computational performance of the BMMR implemented in a moving particle semi-implicit (MPS) simulation system are verified by using benchmark test cases of 2D free surface flows.

End-to-End Multi-Resolution 3D Capsule Network for People Action Detection

In this paper, we propose an end-to-end multi-resolution three-dimensional (3D) capsule network for detecting actions of multiple actors in a video scene. Unlike previous capsule, network-based action recognition does not specifically concern with the individual action of multiple actors in a single scene, our 3D capsule network takes advantage of multi-resolution technique to detect different actions of multiple actors that have different sizes, scales, and aspect ratios. Our 3D capsule network is built on top of 3D convolutional neural network (3DCNN) that extracts spatio-temporal features from video frames inside regions of interest generated by Faster RCNN object detection. We first apply our method to the problem of detecting illegal cheating activities in a classroom examination scene with multiple subjects involved. Second, we test our system on the publicly available and extensively studied UCF-101 dataset. We compare our method with several state-of-the-art 3DCNN-based methods, first the multi-resolution 3DCNN, the single-resolution 3D capsule network, and a combination of both these models. We show that models containing 3D capsule networks have a slight advantage over the conventional 3DCNN and multi-resolution 3DCNN. Our 3D capsule networks not only perform a classification of said actions but also generate videos of single actions. Our experimental results show that the use of multi-resolution pathways in the 3D capsule networks make the result even better. Such findings also hold even when we use pre-trained C3D (convolutional 3D) features to train these networks. We believe that the multiple resolutions capture lower-level features at different scales. At the same time, the 3D capsule layers combine these features in more complex ways than conventional convolutional models.

An improved multiresolution technique for pavement texture image evaluating

An improved multiresolution technique for pavement texture image evaluating

Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transforms

In recent years, due to the tremendous development that took place on the Internet and its applications in the aspects of human life, the demands of using digital images have also increased dramatically, and that opens up horizons for scientific research in the field of improving the quality of the digital images by removing the noise that caused by to processes that are applied within network transmissions like performing storage, retrieval, and encryption to preserve privacy. All these effects are yielded to reduce image quality and loss of visual information. To surmount this problem, image enhancement methods are used to eliminate the noise while preserving supreme exact details and essential characteristics as much as possible in the digital image. The wavelet image enhancement technique played a critical role in this field by attempts to reduce noise in the image while retaining the vital features of the image due to the capability to separate the image into sub-bands (sub-images) and influence the frequencies of each sub-band separately, where acquiring the original image content is essential to obtaining reliable performance.
 Different enhancement techniques have been realized by many researchers so far. Each technique has its own privileges and shortcomings. In this work, a proposed procedure is presented and effectuated to the image modified by Additive White Gaussian Noise (AWGN). The proposed enhancement operation was achieved using the combination of Histogram Equalization with a two-dimensional stationary discrete wavelet transform (2D-SWT) as a multi-resolution analysis technique in image processing at three levels of decomposition to obtain revised results of the method of noise removing. To distinguish and eliminate noise from affected pixel points in the wavelet domain the 2D-SWT is used based on the hard and soft threshold systems on both high and low frequencies to decrease noise from the noisy image. Then, the multi-level 2D inverse wavelet transform (2D-IWT) is applied to eliminate noise and complete the synthesis of the image by the proposed image enhancement techniques.
 In the end, the performance of the proposed methods has been evaluated by using the Peak Signal to Noise Ratio (PSNR). Experimental measurements determine that the results of the proposed techniques enhanced the PSNR by about (16.16%) with respect to the results of the related works, and the structure of the image quality has also been improved in terms of edges retaining and greater noise elimination.

Assessment of gust factors and wind speed decomposition methods for thunderstorms

This paper examines wind decomposition methods for nonstationary thunderstorms based on the frequency responses. A wide range of methods have been used for this task, including weighted-average methods, Savitzky-Golay filters, and multi-resolution techniques. Two problems arise in the evaluation: (1) how to decide which methods are good, and (2) how to compare the wind properties from one decomposition to another. To determine the most suitable method, the overshoot and sharpness of their frequency responses are evaluated and compared. The half-power cutoff frequency is proposed to connect different methods, so that the wind properties, e.g., gust factor, can be compared. The choice of the cutoff frequency for thunderstorms is also examined with artificially generated nonstationary winds. Records from four thunderstorms are decomposed with the different methods and cutoff frequencies. The gust factors with different gust averaging times are calculated and compared with those for stationary winds. Underlying differences between the gust factor curves are also examined. Finally, the paper provides recommendations for the selection of decomposition method and cutoff frequency for nonstationary winds like those in thunderstorms.

Developing novel video coding model using modified dual-tree wavelet-based multi-resolution technique

Developing novel video coding model using modified dual-tree wavelet-based multi-resolution technique

A New Biomedical Image Denoising Method Using an Adaptive Multi-resolution Technique

In the world of digital image processing, image denoising plays a vital role, where the primary objective was to distinguish between a clean and a noisy image. However, it was not a simple task. As a consequence of everyone's understanding of the practical challenge, a variety of methods have been presented during the last few years. Of those, wavelet transformer-based approaches were the most common. But wavelet-based methods have their own limitations in image processing applications like shift sensitivity, poor directionality, and lack of phase information, and they also face difficulties in defining the threshold parameters. As a result, this study provides an image de-noising approach based on Bi-dimensional Empirical Mode Decomposition (BEMD). This project's main purpose is to disintegrate noisy images based on their frequency and construct a hybrid algorithm that uses existing de-noising techniques. This approach decomposes the noisy picture into numerous IMFs with residue, which were subsequently filtered independently based on their specific properties. To quantify the success of the proposed technique, a comprehensive analysis of the experimental results of the benchmark test images was conducted using several performance measurement matrices. The reconstructed image was found to be more accurate and pleasant to the eye, outperforming state-of-the-art denoising approaches in terms of PSNR, MSE, and SSIM.

Fault Detection in Cluster Microgrids of Urban Community using Multi Resolution Technique based Wavelet Transforms

Fault Detection in Cluster Microgrids of Urban Community using Multi Resolution Technique based Wavelet Transforms

Change Detection of Building Objects in High-Resolution Single-Sensor and Multi-Sensor Imagery Considering the Sun and Sensor’s Elevation and Azimuth Angles

Building change detection is a critical field for monitoring artificial structures using high-resolution multitemporal images. However, relief displacement depending on the azimuth and elevation angles of the sensor causes numerous false alarms and misdetections of building changes. Therefore, this study proposes an effective object-based building change detection method that considers azimuth and elevation angles of sensors in high-resolution images. To this end, segmentation images were generated using a multiresolution technique from high-resolution images after which object-based building detection was performed. For detecting building candidates, we calculated feature information that could describe building objects, such as rectangular fit, gray-level co-occurrence matrix (GLCM) homogeneity, and area. Final building detection was then performed considering the location relationship between building objects and their shadows using the Sun’s azimuth angle. Subsequently, building change detection of final building objects was performed based on three methods considering the relationship of the building object properties between the images. First, only overlaying objects between images were considered to detect changes. Second, the size difference between objects according to the sensor’s elevation angle was considered to detect the building changes. Third, the direction between objects according to the sensor’s azimuth angle was analyzed to identify the building changes. To confirm the effectiveness of the proposed object-based building change detection performance, two building density areas were selected as study sites. Site 1 was constructed using a single sensor of KOMPSAT-3 bitemporal images, whereas Site 2 consisted of multi-sensor images of KOMPSAT-3 and unmanned aerial vehicle (UAV). The results from both sites revealed that considering additional shadow information showed more accurate building detection than using feature information only. Furthermore, the results of the three object-based change detections were compared and analyzed according to the characteristics of the study area and the sensors. Accuracy of the proposed object-based change detection results was achieved over the existing building detection methods.

Influence of the multi-resolution technique on tomographic reconstruction in ultrasound tomography

The greatest advantage of scattering theory-based ultrasound tomography (UT) is its ability to investigate small structures. DBIM is the Distorted Born Iterative Method. The nearest neighbour interpolation method is used to enhance the reconstruction performance and reduce the reconstruction time. The raw (N 1 × N 1) and dense (N 2 × N 2) meshed integration areas are reconstructed in NN 1 and NN 2 iterations, respectively. However, choosing the best value of NN 1 to get the highest performance was not mentioned in previous works. If it is not well chosen, the reconstruction quality is even worse than that when using no interpolation. This study proposes a method to enhance the UT reconstruction by using the nearest neighbour interpolation (MR-DBIM). The corresponding algorithms are specified by the graphical concurrent programming language of Sleptsov nets. Some significant results are (1) the MR-DBIM is only meaningful when (i.e. sparse scattering domain); (2) the best performance is obtained in the DBIM when Nt = Nr , but in the MR-DBIM when Nr = 2Nt ; (3) the well-investigated value of NN 1 is 2 when and is 3 when .

Effect of Land Surface Temperature on Urban Heat Island in Varanasi City, India

Crucial changes in urban climate can be witnessed due to rapid urbanisation of cities across the world. It is important to find a balance between urban expansion and thermal environment quality to guarantee sustainable urban development. Thus, it is a major research priority to study the urban heat island (UHI) in various fields, i.e., climate change urban ecology, urban climatology, urban planning, mitigation and management, urban geography, etc. The present study highlighted the interrelationship between land surface temperature (LST) and the abundance of impervious cover and green cover in the Varanasi city of Uttar Pradesh, India. For this purpose, we used various GIS and remote-sensing techniques. Landsat 8 images, land-use–land-cover pattern including urban/rural gradients, and grid- and metric-based multi-resolution techniques were used for the analysis. From the study, it was noticed that LST, density of impervious cover, and density of green cover were correlated significantly, and an urban gradient existed over the entire city, depicting a typical UHI profile. It was also concluded that the orientation, randomness, and aggregation of impervious cover and green cover have a strong correlation with LST. From this study, it is recommended that, when planning urban extension, spatial variation of impervious cover and green cover are designed properly to ensure the comfort of all living beings as per the ecological point of view.