Directional Filter Research Articles

Combined ARFI and Shear Wave Imaging of Prostate Cancer: Optimizing Beam Sequences and Parameter Reconstruction Approaches.

This study demonstrates the implementation of a shear wave reconstruction algorithm that enables concurrent acoustic radiation force impulse (ARFI) imaging and shear wave elasticity imaging (SWEI) of prostate cancer and zonal anatomy. The combined ARFI/SWEI sequence uses closely spaced push beams across the lateral field of view and simultaneously tracks both on-axis (within the region of excitation) and off-axis (laterally offset from the excitation) after each push beam. Using a large number of push beams across the lateral field of view enables the collection of higher signal-to-noise ratio (SNR) shear wave data to reconstruct the SWEI volume than is typically acquired. The shear wave arrival times were determined with cross-correlation of shear wave velocity signals in two dimensions after 3-D directional filtering to remove reflection artifacts. To combine data from serially interrogated lateral push locations, arrival times from different pushes were aligned by estimating the shear wave propagation time between push locations. Shear wave data acquired in an elasticity lesion phantom and reconstructed using this algorithm demonstrate benefits to contrast-to-noise ratio (CNR) with increased push beam density and 3-D directional filtering. Increasing the push beam spacing from 0.3 to 11.6 mm (typical for commercial SWEI systems) resulted in a 53% decrease in CNR. In human in vivo data, this imaging approach enabled high CNR (1.61-1.86) imaging of histologically-confirmed prostate cancer. The in vivo images had improved spatial resolution and CNR and fewer reflection artifacts as a result of the high push beam density, the high shear wave SNR, the use of multidimensional directional filtering, and the combination of shear wave data from different push beams.

Non-Reciprocal MEMS Periodic Structure

In recent years, active periodic structures with in-time modulated parameters have drawn ever-increasing attention due to their peculiar (and sometimes exotic) wave propagation properties. Although many experimental works have shown the efficacy of time-modulation strategies, the benchmarks proposed until now have been mostly proof-of-concept demonstrators, with little attention to the feasibility of the solution for practical purposes. In this work, we propose a micro electro-mechanical system (MEMS) periodic structure with modulated electromechanical stiffness featuring non-reciprocal band-gaps that are frequency bands where elastic waves are allowed to travel only in one direction. To this aim, we derive a simplified analytical lumped-parameter model, which is then verified through numerical simulations of both the lumped-parameter system and the high-fidelity multiphysics finite element model including electrostatic effects. We envision that this system, which can easily be manufactured through standard MEMS production processes, may be used as a directional filter in MEMS devices such as insulators and circulators.

AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages.

A study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre-processing layer that takes X-ray images and the age as the input is proposed. A total of 1018 cephalometric radiographs were labelled and classified according to the CVM stages. The images were separated according to gender for better model-fitting. The images were cropped to extract the cervical vertebrae automatically using an object detector. The resulting images and the age inputs were used to train the proposed DL model: AggregateNet with a set of tunable directional edge enhancers. After the features of the images were extracted, the age input was concatenated to the output feature vector. To have the parallel network not overfit, data augmentation was used. The performance of our CNN model was compared with other DL models, ResNet20, Xception, MobileNetV2 and custom-designed CNN model with the directional filters. The proposed innovative model that uses a parallel structured network preceded with a pre-processing layer of edge enhancement filters achieved a validation accuracy of 82.35% in CVM stage classification on female subjects, 75.0% in CVM stage classification on male subjects, exceeding the accuracy achieved with the other DL models investigated. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If AggregateNet is used without directional filters, the test accuracy decreases to 80.0% on female subjects and to 74.03% on male subjects. AggregateNet together with the tunable directional edge filters is observed to produce higher accuracy than the other models that we investigated in the fully automated determination of the CVM stages.

An Anti-Noise Fast Circle Detection Method Using Five-Quadrant Segmentation

Extracting circle information from images has always been a basic problem in computer vision. Common circle detection algorithms have some defects, such as poor noise resistance and slow computation speed. In this paper, we propose an anti-noise fast circle detection algorithm. In order to improve the anti-noise of the algorithm, we first perform curve thinning and connection on the image after edge extraction, then suppress noise interference by the irregularity of noise edges and extract circular arcs by directional filtering. In order to reduce the invalid fitting and speed up the running speed, we propose a circle fitting algorithm with five quadrants, and improve the efficiency of the algorithm by the idea of "divide and conquer". We compare the algorithm with RCD, CACD, WANG and AS on two open datasets. The results show that we have the best performance under noise while keeping the speed of the algorithm.

Mapping and analysis of structural lineaments using SRTM radar data and Landsat-8 OLI image: an example from the Telouet–Tighza area, Marrakech High Atlas, Morocco

This study provides the first evaluation of the potential of both the Landsat-8 Operational Land Imager (OLI) sensor images and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data for automated lineament extraction in the south side of the Marrakech High Atlas (Telouet–Tighza area). After image corrections, enhancement methods such as principal component analysis, band composite (BC), and directional filtering were adopted to create new images that provided high visibility of linear structures. The new Landsat BC image used in this study was selected based on the calculation of the optimum index factor and correlation index. In addition to the Landsat image, the SRTM DEM was used to detect structural lineaments in the area by generating shaded relief images. Multisource data, such as band ratio image, geological maps, and fieldwork, were used to eliminate the nongeological lineaments extracted. The results indicate that an automated method was applied successfully for lineament mapping in this area, by detailing the main tectonic faults. Moreover, new lineaments are identified and are validated by fieldwork. Structural lineaments extracted show compatibility in their direction, length, distribution, and density with the tectonic evolution of the study area. A total of 2945 lineaments were extracted with major ENE–WSW and predominant E–W directions. The new structural map shows more structural information compared with the geological map of this area and exemplifies the performance of Landsat-8 OLI bands and SRTM data in this kind of study.

Spreading Magnetic Anomalies Separation of the South China Sea Based on the Nested Elliptical Directional Filters

Spreading magnetic anomalies recorded the paleo-geomagnetic field variation that has great significance in the investigation of the extension process of ocean basins. Interpreting spreading magnetic anomalies under complex geological environments is challenging, especially for marginal sea basins. We proposed nested elliptical directional filters to separate the spreading magnetic anomalies of the South China Sea (SCS). The results show that the spreading magnetic anomalies separated by the nested elliptical directional filters depict the expansion process of the oceanic crust, and the interference magnetic anomalies are effectively suppressed. The separated spreading magnetic anomalies indicate that the expansion process of the SCS is affected by the interactions between the surrounding plates. The spreading magnetic anomalies of the SCS are warped, interrupted, and not strictly parallel. The pattern of the spreading magnetic anomalies reflects multiple ridge jumps during the expansion process and the post-spreading magmatic disturbances. The long-wavelength magnetic anomalies indicate lithospheric fractures and Curie surface variations in the SCS, which are affected by the post-spreading magmatic rejuvenation. The magnetic anomalies of the SCS resulted from the superposition of magnetic anomalies in the ocean crust and the uppermost mantle.

Polarized Shack-Hartmann wavefront sensor

Shack-Hartmann wavefront sensor (SHWFS) has been widely used in adaptive optics (AO) systems to detect phase distortion characteristics. In laser communication, target detection, vision optics and other application fields, the performance of SHWFS is affected by bright skylight and scattered light, which restricted the working ability of the AO system severely. Therefore, this paper proposes a new polarized SHWFS (p-SHWFS) based on the principle of polarization imaging, which utilize the difference of state of polarization (SoP) between signal and stray light to improve the image contrast. The p-SHWFS can be composed simply by a micro-lens array and a linear polarization camera. The camera uses four directional polarizing filters at 0°, 135°, 45° and 90° on every four pixels. Thus, the degree of linear polarization (DoLP) and angle of linear polarization (AoLP) for the incident wavefront can be analyzed, and the signal-to-background ratio (SBR) can be improved in some certain depending on the difference of SoP. In this paper, we introduce the basic principle of the p-SHWFS and validate the feasibility and accuracy improvement by numerical simulation and practical experiments. The experimental results show that the p-SHWFS can improve obviously the measurement accuracy under strong stray light when the difference of SoP exists. That may give us some initial reference to reduce the influence of stray light in laser communication, target detection, vision optics and other application fields.

Image Interpolation Based on Spiking Neural Network Model

Image interpolation is used in many areas of image processing. It is seen that many techniques developed to date have been successful in both protecting edges and increasing image quality. However, these techniques generally detect edges with gradient-based linear calculations. In this study, spiking neural networks (SNNs), which are known to successfully simulate the human visual system (HVS), are used to detect edge pixels instead of the gradient. With the help of the proposed SNN-based model, the pixels marked as edges are interpolated with a 1D directional filter. For the remaining pixels, the standard bicubic interpolation technique is used. Additionally, the success of the proposed method is compared to known methods using various metrics. The experimental results show that the proposed method is more successful than the other methods.

Sparse representation-based demosaicking method for joint chromatic and polarimetric imagery

Division of focal plane (DoFP) polarization camera has become mainstream among various polarimetric imaging systems whose sensor is equipped with Bayer filter and directional polarization filter, so joint chromatic and polarimetric image demosaicking is essential. However, since 15 or 14 pixels are missed out of 16 pixels in color polarization mosaic images, it’s challenging to adopt a method to reconstruct full-resolution images well. In this paper, we propose a demosaicking model based on sparse coding. The model introduces RGB-polarization channels correlation, adaptive sub-dictionaries and non-local self-similarity restrictions, and combines them into an optimal problem to solve. The experimental results, including reconstructing full-resolution synthetic images and camera captured real scenes, demonstrate that our proposed method outperforms the current state-of-the-art method in terms of quantitative measures and visual quality.

Identification of Fault Zone in Bali Using GGMPlus Gravity and Alos-2 Palsar-2 Data

The local active fault in Bali has a small magnitude (M<5) but has destructive potential because it is very close to residential areas. Mapping the fault area on Bali is needed to identify the parameters of faults. This study used gravity data from GGMplus, topographic data from DEMNAS, and lineaments using ALOS-2 PALSAR-2 data. Validation and interpretation using the geological map of Bali and seismicity data. We interpret the subsurface using the gravity derivative method to identify the type of fault movement. Identify fault locations using lineament extraction from SAR data processed by directional filters. The composite image red-green-blue (RGB) for HH, HV, and VV polarization was used for automatic lineament extraction and then corrected manually. The results of the gravity method succeeded in identifying 29 of the 30 faults from the geological map of the Bali sheet and a new spot from PALSAR-2. Bali land has 12 thrust faults, 11 strike-slip faults and six normal faults. The image of PALSAR-2 (L band) has succeeded in making a fault lineament map for the Bali region. The lineament extraction results from PALSAR-2 obtained four new faults (Pesanggaran, Sepang, Tegal Badeng, and Banyuwedang), while four faults were not identified (Tampaksiring Fault, Plaga, Mambal, and Munduk-Rajasa). NE-SW dominates the strike directions, and the dip angles are 45-80 degrees. We propose 30 faults in Bali, including 26 defects from geological maps with changes in length and location shift and four new marks extracted from automatic lineament.Keywords: Remote Sensing, Earthquake, Derivative Gravity, Lineament, SAR

Structural Study of the Precambrian Basement of Kaele (Far North Cameroon): Contribution of Remote Sensing (Application of Landsat 8 OLI/TIRS Images) and Field Data

The Kaele is located in the northern domain of the Pan-African fold belt of Central Africa in Cameroon, north of Mayo- Kébbi domain. The structural study carried out in this area is based on Landsat 8 OLI/ TIRS image processing techniques and traditional geological prospection methods. The objective is to evaluate the contribution of landsat 8 OLI/TIRS image processing and field data in the structural mapping of the Kaélé region. The application of the 7×7 directional filters of the Sobel type in the N-S, E-W, NE-SW and NW-SE directions, to the ETM+3 channels gives better results. The rose diagram of the lineament map (obtained from image processing) shows a major E-W direction (N100E) and a secondary direction N-S (N10E). The studied area is affected by a polyphase deformation D1 to D4. The first phase of deformation (D1) is tangential and has set up the sub-horizontal dipping S1 schistosity which bears a composite L1 lineation (stretching, mineral). The D2 phase is constrictional and responsible for the establishment of the subvertical S2 axial plane foliation of the P2 folds, carrying the composite L2 lineation (stretching, mineral) and the B2 boudinage. The dextral-moving C2 shear cuts the S1 foliation, the P2 folds are asymmetrical isopach and anisopach. The third phase D3 is a constriction and set up the S3 schistosity which appears in syn -tectonic granite and highlight the C3 shear planes. The C3 shear is dextral and the P3 folds are isopach, anisopach, intrafolial and sheath fold. The last phase of deformation D4 is brittle and has set up faults and joints. There exist similarities between orientation of lineament from image processing and direction of D2, D3 and D4 deformational phases structures.

Scalable Antenna Arrays Featuring Stable Radiation Pattern in Multioctave Bandwidth

This article proposes a novel concept of antenna arrays operating in the multiple-octave frequency range. The antenna arrays ensure steady half-power beamwidth (HPBW) in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{N}$ </tex-math></inline-formula> :1 bandwidth (BW) due to the proper placement of radiating elements and appropriate signal switching between them. The proposed arrays consist of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N+1$ </tex-math></inline-formula> centered subarrays that are electrically similar at successive frequencies. The antenna arrays are fed by networks composed of power dividers and directional filters responsible for gradual RF power shift from the elements operating at the lowest frequency to the elements operating at the highest frequency. The directional filters utilize the same properly scaled switching function for every frequency octave in the operational frequency range. The presented concept has been verified by the design of antenna arrays operating over 1.2–4.8 (4:1 BW) and 1.2–9.6 GHz (8:1 BW) frequency ranges.

Techniques to establish accurate background noise levels in areas affected by operational wind turbine noise

During the planning process for an onshore wind farm, the establishment of accurate background noise levels at nearby noise sensitive receptors is vital in determining the allowable noise limits at these locations. This is typically achieved through background noise monitoring at a selection of representative noise sensitive receptors. As onshore windfarms have become more prevalent, areas suitable for wind farm development are increasingly within the vicinity of operational wind farms. The result of this is background noise monitoring at receptors is now often influenced by nearby operational wind farms and is not a true representation of the unaffected noise levels at these locations. Current good UK practice (as set out in the Institute of Acoustics document 'A good practice guide to the application of ETSU-R-97 for the assessment and rating of wind turbine noise') proposes a number of solutions to account for this additional wind farm noise. One solution is to consider directional filtering or the subtraction of the predicted noise levels from the measured baseline data. Whilst both approaches are valid, it can often be unclear which approach is most appropriate for a particular scenario. Within this paper, the limitations of current good practice are discussed, and an alternative methodology is proposed which combines both approaches to establish the unaffected background noise levels. Specific examples of where this alternative approach has been used are presented within the paper as validation.

Generation and directional decomposition of guided waves for finite-range defect detection in rail tracks

Abstract Railway tracks exhibit a complex, sudden alteration in their cross-sectional configuration. Generating guided waves within thick rail tracks using conventional guided wave transducers is challenging. This research employs directional decomposition to comprehend how guided waves form within the rail. These waves arise from constructive interference between multiple reflected bulk waves, which are induced by point forces on the top surface of the railhead or diffracted from cracks in the rail structure. This study simulates finite-range detection of rail defects using finite element analysis to demonstrate a potential application to the guided wave rail inspection car. The transmitter and a series of monitoring points are located on the same side of the rail defect in the pitch-catch configuration. The transmitting guided waves often hide the small backscatter waves induced by defects. A directional filter extracts the small backscatter signals from the entire received data. A 100 kHz tone burst actuates on the top surface of the rail in three orthogonal directions. Numerical findings demonstrate that vertical and tangential excitations are suitable for detecting defects in the railhead and lower region, while transverse excitation and detection are appropriate for identifying defects in the rail web. The application of directional decomposition provides valuable insights into the complex process of backscatter waves arising from rail track defects.

Nonlinear ultrasonic characterization of joint structures using backscatter guided waves

Abstract Joint structures made from plates or shells are frequently used in large constructions, such as oil storage tanks in petrochemical industries. The corrosion-induced metal loss often appears on the back side of the bottom plate supported by ground or sand. The welding residual stress and weight of shell walls accelerate the corrosion. Determining the severity of metal loss under the joint is challenging. This work uses the mode pair technique for nondestructive inspection of inaccessible regions in joint structures. A finite element analysis model simulates the transient responses of the incident and mode-converted fundamental and second harmonic plate waves. The second harmonic s0 plate wave satisfying the phase velocity matching condition accompanies the transmitted fundamental SH0 plate wave. A directional filter identifies every mode-converted plate wave by comparing the frequency spectra with the dispersion curves. Linear ultrasonic guided waves can detect inaccessible regions using mode-converted reflections. The symmetry of the linear mode-converted fundamental plate waves depends on the defect position and geometry, not the size. The mode-converted second harmonic guided wave can achieve better spatial resolution for defect sizing. The nonlinear parameter shows a proportional trend with an increasing thickness reduction. Experimental and numerical evidence reveals nonlinear ultrasonic guided wave technique has the potential for defect detection in joint structures.

A Novel Method for Eliminating the Strip-Shaped Interferences in Aeromagnetic Anomaly Based on Convolutional Neural Network

The strip-shaped interferences in aeromagnetic data pose great adverse effects on data interpretation. In literature, a number of methods have been proposed to deal with this problem. However, existing methods still have some limitations. Inspired by the ability of deep learning techniques to extract features from data, this study presents a novel convolutional neural network (CNN)-based method to eliminate the strip-shaped interferences in aeromagnetic data. The proposed method uses the U-Net structure to establish the whole network. The use of up–down sampling and skip connection enables the network to extract multiscale strip-shaped interferences with complex distribution and morphological characteristics. The basic theoretical formulas of the network and its architecture are presented, along with the construction method of the training dataset. Afterward, the presented method is tested on several synthetic examples and real aeromagnetic data collected in Jining, Inner Mongolia, and is compared with the conventional directional cosine filter to display its advantage in accuracy. The results demonstrate that the presented method can eliminate the strip-shaped interferences in aeromagnetic data effectively while preserving the features due to the real geological sources without any subjective parameters.

Adaptive Noise Detector and Partition Filter for Image Restoration

The random-value impulse noise (RVIN) detection approach in image denoising, which is dependent on manually defined detection thresholds or local window information, does not have strong generalization performance and cannot successfully cope with damaged pictures with high noise levels. The fusion of the K-means clustering approach in the noise detection stage is reviewed in this research, and the internal relationship between the flat region and the detail area of the damaged picture is thoroughly explored to suggest an unique two-stage method for gray image denoising. Based on the concept of pixel clustering and grouping, all pixels in the damaged picture are separated into various groups based on gray distance similarity features, and the best detection threshold of each group is solved to identify the noise. In the noise reduction step, a partition decision filter based on the gray value characteristics of pixels in the flat and detail areas is given. For the noise pixels in flat and detail areas, local consensus index (LCI) weighted filter and edge direction filter are designed respectively to recover the pixels damaged by the RVIN. The experimental results show that the accuracy of the proposed noise detection method is more than 90%, and is superior to most mainstream methods. At the same time, the proposed filtering method not only has good noise reduction and generalization performance for natural images and medical images with medium and high noise but also is superior to other advanced filtering technologies in visual effect and objective quality evaluation.

Electrostatic deposition of TiO2 nanoparticles on porous wood veneer for improved membrane filtration performance and antifouling properties

Wood has been a promising water purifier material on account of its abundant natural transport channels, easy processing, and renewability, which is mainly focused on its utilization in growth direction for effective separation.Wood veneer manufacured from raw wood block has a reversed-tree pore structure, and possesses advantages of low cost, easy fabrication, material saving, and abundant sources. To realize its functionalization and practicable application for membrane separation, modification of wood veneer is prerequisite. Herein, thin wood veneer with disparate utilization direction of wood was developed to design filter membrane loading TiO2 nanoparticles for treatment of dye wastewater. Wood veneer with reversed-tree transport pathways exhibits unique porous structure, and filtering direction and wood growth direction is almost orthogonal generated numerous sinuous channels. Thereout, sufficient area for loading TiO2 nanoparticles and contacting pollutants as well as appropriate water transport pathways at significantly shrinking thickness of wood (the thickness of 0.2 mm) can be provide by these sinuous channels. TiO2 nanoparticles was first modified by (3-Aminopropyl)triethoxysilane with high positive charge, and immobilized on negatively charged wood surface through atmospheric impregnation via strong electrostatic attractive interaction. Vast quantities of exposed TiO2 nanoparticles on wood cell lumens significantly enhance the adsorption ability for dye contaminants, resulting in a high membrane separation performance. The flux of TiO2/wood veneer membrane can achieve high level of 636.94 L/(m2h) with considerable methylene blue removal of 99.9% at 0.01 MPa. Meanwhile, it shows good cycling stability as well as decent flexibility and excellent mechanical strength. Moreover, the designed membrane with photocatalytic function of TiO2 also displays impressive decontaminated and recycling ability. The flux can recover its pre-recession level after 10 h light irradiation. The designed TiO2/wood veneer with simple preparation process and excellent water treatment capacity exhibits promising results for practical wastewater treatment.

A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of Iran

One of the specific features of porphyry copper (Cu) mineralization is the distinct occurrence of hydrothermal alteration zones, which can be mapped by processing various satellite images. Among free multispectral images, Landsat 8 OLI and ASTER data are known to be efficient in mapping different geological features, such as alteration zones and tectonic lineaments. This study aims to show the potential of these data types in mapping porphyry Cu mineralization by proposing a framework for employing and integrating different image processing methods. These methods include principal component analysis (PCA), spectral angle mapper (SAM), and matched filtering (MF) employed on these satellite images to map target al.teration zones. Moreover, PCA and directional filtering are applied to the ASTER dataset to enhance and map structural features. The results are evaluated and then combined to provide a potential map of Cu mineralization in the Chahargonbad area, located within the Urumieh-Dokhtar magmatic belt (UDMB) in Kerman province, Iran. The prediction-area plot and normalized density, which are data-driven methods, are used to assign the relative weight of each layer and evaluate them. Finally, using the calculated weights, data-driven multi-index overlay (DMIO) and fuzzy ordered weighted averaging (FOWA) methods are applied to combine the evidential layers. The potential mineralization maps created by the DMIO and FOWA provide a prediction rate of 80% and 82%, respectively. Furthermore, the accuracy of the integrated maps is investigated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. The AUC scores obtained from the ROC curves of DMIO and FOWA methods are 0.85 and 0.88, respectively, representing powerful positive spatial relationships with mineralization areas. Based on the results, the proposed framework can be applied to provide a potential map of porphyry Cu mineralization, particularly in arid regions, with reasonable accuracy.

Noise Removal from Images Using Adaptive Neuro/Network-Fuzzy Interface Systems

Any Information signal is best desirable without any external noise/ disturbances. Noise in any signal is the undesirable quantity present which deteriorates the signal's quality, thus compromising the information. Any signal, be it an image signal (2-D) or else a video signal (3-D) in the field of communication, if not always but most number of times prone to noise. In this paper, we would be dealing with removing types of noise on an image, using various filter techniques such as vector median filter, vector directional filter. Using the image processing tools in MATLAB, we could achieve this quite effortlessly. Looking at the prior approaches and keeping those factors in understanding, this paper would intend to path a more thoughtful way to bifurcate the image from its noise using the techniques of the neural networks. With thorough scrutiny and understanding of filters, this paper ensemble the performance of filters, through which we would be applying the most suitable filter out of the lot with the neural networks.