High Noise Research Articles

Multi-Scale Marine Object Detection in Side-Scan Sonar Images Based on BES-YOLO.

Aiming at the problem of low accuracy of multi-scale seafloor target detection in side-scan sonar images with high noise and complex background texture, a model for multi-scale target detection using the BES-YOLO network is proposed. First, an efficient multi-scale attention (EMA) mechanism is used in the backbone of the YOLOv8 network, and a bi-directional feature pyramid network (Bifpn) is introduced to merge the information of different scales, finally, a Shape_IoU loss function is introduced to continuously optimize the model and improve its accuracy. Before training, the dataset is preprocessed using 2D discrete wavelet decomposition and reconstruction to enhance the robustness of the network. The experimental results show that 92.4% of the mean average accuracy at IoU of 0.5 (mAP@0.5) and 67.7% of the mean average accuracy at IoU of 0.5 to 0.95 (mAP@0.5:0.95) are achieved using the BES-YOLO network, which is an increase of 5.3% and 4.4% compared to the YOLOv8n model. The research results can effectively improve the detection accuracy and efficiency of multi-scale targets in side-scan sonar images, which can be applied to AUVs and other underwater platforms to implement intelligent detection of undersea targets.

Are workers also vulnerable to the impact of ambient air pollution? Insight from a large-scale ventilatory exam

It remains unclear how ambient air pollution may affect the prevalence of obstructive ventilatory dysfunction (OVD) among workers. We aim to assess the association of a comprehensive set of ambient air pollutants with OVD prevalence in workers and to explore the potential interactive effects of the occupational factors. This is a population-based cross-sectional study among 305,022 participants from the Guangdong Province, China. Mixed-effects models were used to obtain differences in the OVD risk associated with a 10 μg/m3 increase in ambient air pollution. We found that for each 10 μg/m3 increase in PM2.5, PM10, PM coarse, O3, and NO2 concentrations, the odds ratio (OR) for OVD in workers is 1.324 (95 % confidence interval (CI), 1.282–1.367), 1.292 (95 % CI, 1.268–1.315),1.666 (95 % CI, 1.614–1.719), 1.153 (95 % CI, 1.142–1.165), and 1.023 (95 % CI, 1.012–1.033). We observed that young participants (18–38 years old), women, participants with longer years of service (>48 months), participants working in large enterprises, professional skills workers, and production and manufacturing workers have higher estimated effects. In addition, we also found that workers exposed to high temperatures have higher estimated effects under air pollutants exposure, while workers exposed to noise have higher estimated effects under PM2.5, PM10, NO2, and O3 exposure. Workers exposed to dust have a lower risk of developing OVD under exposure to ambient air pollutants compared to those not exposed. Our results indicate that ambient air pollution increases the risk of OVD in workers. Moreover, air pollutants exhibit a greater estimated effect among workers exposed to high temperatures or noise. Our research findings highlight the importance of fully considering the impact of ambient air pollution on protecting the respiratory health of workers.

A Scoping Review on Occupational Noise Mitigation Strategies and Recommendations for Sustainable Ship Operations.

Environmental and occupational noise has the potential to result in health risks. The presence of high noise levels aboard ships can cause substantial hazards that affect the well-being of those employed in the maritime industry. The study and implementation of occupational noise reduction aboard ships are of the highest priority for ensuring the well-being of marine workers, compliance with regulatory standards, protection of the environment, and improvement of overall operational efficiency and safety within the maritime sector. A scoping study was conducted to collect and summarize the existing scientific literature about approaches to preventing occupational noise in vessel operations. We searched electronic databases for papers published up to June 2024. Initially, 94 articles were identified for screening, and the present research produced 16 studies, which were finally analyzed. Resultantly, noise control may begin with elimination, substitution, engineering, administrative, and hearing protection (ear plugs or muffs). Noise control innovation would be started with engineering techniques. Hearing protection devices (HPDs) could be used to reduce noise and as an instrument of communication between sailors. More research needs to be carried out in order to find the best ways for maritime vessels to reduce noise at work and to see how well they work in lowering the risks that come with noise for workers on board.

Trade-off and chaotic dynamics in a two-prey one-predator model with refuge, environmental noise and seasonal effects

In nature, poisonous prey species have developed toxins as an attack against predators. However, predator’s decisions to consume aposematic toxic prey are not solely determined by their toxicity. Predators often weigh the costs of ingesting toxins against the benefits of obtaining nutrients, with body size acting as a significant cue. In terms of avoiding predation, prey commonly respond by seeking refuge, a behavior that fluctuates considerably due to seasonality and environmental variability. In this study, we integrate these factors into the dynamics of two prey and one predator model. We explore the cost of fear to illustrate the trade-off dynamics of prey, and we adjust the predator’s death rate by integrating a function that serves predator loss resulting from encounters with toxic prey. Our model demonstrates bistability as a consequence of hysteresis effect and undergoes various bifurcations, including saddle–node, Hopf, Bautin, and cusp bifurcations. We observe that increasing prey refuge and reducing the level of fear contribute to system stabilization, yet chaotic behavior persists with respect to both parameters. While seasonality can introduce stability and periodicity, it may also give rise to complex and unpredictable dynamics. We comprehend that the system might undergo transitions through multiple periodic cycles as a result of changes in the degree of seasonality, yet it eventually settles into a periodic solution. Furthermore, our analysis using the stochastic model reveals that low noise levels maintain species densities close to deterministic equilibrium, whereas high noise levels result in broader dispersion or even extinction of species. The information presented in this article may help readers understand the biological knowledge gained by investigating interactions between predators and prey.

Simultaneous PIV/LIF Measurements Of Velocity And Oxygen In An Advecting Air-Water Channel Flow

We investigate oxygen transfer across an air-water interface using an integrated Particle Image Velocimetry (PIV) and Laser-Induced Fluorescence (LIF) system. This setup allows for the simultaneous measurement of velocity and dissolved oxygen (DO) concentration fields, alongside the visualization of the air-water interface. The imaging process begins with zero DO concentration and continues until oxygen saturation in the water is achieved. Calibration of the oxygen LIF intensity field is performed using these images, benchmarked against measurements from an optical oxygen point probe, to ensure accurate conversion to a physically relevant unit. The air channel generates an air flow with a center-line mean velocity of 8.84 m/s, corresponding to a Taylor Reynolds number of 100. This high-velocity air flow results in the formation of wavy structures on the water interface creating situations similar to those found in nature (such as rivers and oceans). The wavy structures create reflections from the air-water interface; these reflections are mitigated during post-processing using a Wavelet-Fast Fourier Transform (W-FFT) method to remove high intensity noise. Additionally, the wind shear generated by the air flow induces high turbulence intensity and vortices near the air-water interface, significantly enhancing the dissolution of oxygen into the water. This turbulence and the associated vortical structures play a crucial role in the mixing process and the overall oxygen transfer efficiency. The study presents instantaneous dissolved oxygen structures overlapped with velocity structures in a flow with deformable interface, providing some insights into the oxygen transfer and mixing processes.

Sound Absorption Performance of Ultralight Honeycomb Sandwich Panels Filled with "Network" Fibers-Juncus effusus.

This study investigates lightweight and efficient candidates for sound absorption to address the growing demand for sustainable and eco-friendly materials in noise attenuation. Juncus effusus (JE) is a natural fiber known for its unique three-dimensional network, providing a viable and sustainable filler for enhanced sound absorption in honeycomb panels. Microperforated-panel (MPP) honeycomb absorbers incorporating JE fillers were fabricated and designed, focusing on optimizing the absorber designs by varying JE filler densities, geometrical arrangements, and MPP parameters. At optimal filling densities, the MPP-type honeycomb structures filled with JE fibers achieved high noise reduction coefficients (NRC) of 0.5 and 0.7 at 20 mm and 50 mm thicknesses, respectively. Using an analytical model and an artificial neural network (ANN) model, the sound absorption characteristics of these absorbers were successfully predicted. This study demonstrates the potential of JE fibers in improving noise mitigation strategies across different industries, offering more sustainable and efficient solutions for construction and transportation.

An Experimental Investigation Of µUAV Rotor Wingtip Vortices Through High Spatial Resolution PIV

The interest for µ UAVs platforms with VTOL capabilities dramatically rose over the last decade due to the fast-paced advancements in electronics and control. However, the vortical structures shedded from the rotary wings -and their interaction-, which are responsible for high power usage and high noise level emissions, are yet to be fully understood. In this work, an experimental PIV campaign was conducted on a commercially available propeller with a 230mm diameter. The operating angular velocity was chosen to be 5750RPM, producing a chord Reynolds number of about Re_{c,75%} ≃ 11000, generating 3.42N of thrust while requiring 55W of power. The resolution of the PIV setup was able to reliably fit 8 measurement points in the vortex core when using narrowest field-of-view (FOV), while the widest FOV comprises a measurement area of about 0.5D×0.57D. Time averaged as well as phase averaged measurements were performed. The evolution and interaction of the wingtip vortices and shear layers shedded in the flow field is captured with an azimuthal angle step increment of ∆Ψ = 5º. The thrust produced by the multirotor configuration was indeed found to be approximately 5% lower than the one produced by the isolated propeller in hovering conditions. Additionally, the PIV setup successfully met the target spatial accuracy requirements: induced velocities are accurately captured in every stage of the wingtip vortex evolution.

Implementation of diversity combining for radio monitoring of frequency-modulated signals

Formulation of the problem. Radio monitoring of short-range systems is often complicated by the presence of fading due to the complex interference pattern at the receiving point. An effective method of combating fading is a diversity combining. Standard methods of diversity combining require an estimate of the amplitude multiplier and (or) phase shifts when adding signals, which significantly complicates the procedure of spaced reception. The aim of the work is to explore the possibilities of a diversity combining without the need to estimate a complex channel multiplier. Results: the possibility of providing high noise immunity without the need to estimate the radio channel profile for signals with Gaussian frequency-shift keying (GFSK) in conditions of slow flat fading is demonstrated. A method has been developed for receiving GFSK signals, which makes it possible to combine two or more receiving channels to increase noise immunity in multipath conditions. Practical significance. The presented method has a relatively low computational complexity and makes it possible to increase the efficiency of radio monitoring systems.

Improving the efficiency of channel evaluation during high-speed data transmission in underwater acoustic communication with OFDM

Problem statement. The organization of noise-resistant wireless underwater acoustic communication is not a simple and solved task. One of the main problems is the multipath propagation of a sound acoustic wave as a result of re–reflections from the seabed surface and a change in the frequency of signal vibrations when the receiver and transmitter move relative to each other. Thus, finding a channel evaluation solution that can improve the quality of communication is an urgent task for developers of underwater acoustic communication systems. Purpose. To consider the possibility of developing effective methods for processing underwater acoustic communication signals with high noise immunity by evaluating the communication channel. Results. The possibility of using an adaptive filter with a sliding window to evaluate the channel during high-speed data transmission in underwater acoustic communication is shown. The simulation results showed that the proposed adaptive filter with a sliding window surpasses the normalized adaptive filters in terms of the BER bit error and wins over additive filters with an exponential window in reducing the number of mathematical operations. When modeling wireless acoustic communication systems with OFDM, error-free data transmission with a spectral efficiency of 0.33 bits/s/Hz was achieved. Practical significance. The obtained results can be used to improve the wireless transmission of data in underwater acoustic communication due to the proposed algorithm of the adaptive filter.

DRSwin-ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with Shifted Windows

In real industrial environments, acquiring vibration data from bearings is often challenging due to noise, resulting in network models that excel when trained on datasets with sufficient samples but struggle with accurate fault identification in real-world scenarios, inevitably threatening the reliability of fault diagnosis. To address this problem, this paper proposes an end-to-end fault diagnosis framework (DRSwin-ST) based on sparse transformer with a shift window and dynamic threshold noise reduction. The Swin-Transformer serves as the backbone, leveraging a multi-head self-attention mechanism with a shift window to capture global information. The 1.5-Entmax replaces Softmax in the self-attention mechanism, sparsifying irrelevant information and allowing the model to focus on essential details. The self-attention mechanism, combined with a multi-scale structure, forms a forward feedback network to obtain rich fault feature information. In addition, the paper integrates a large convolutional kernel and a dynamic soft-threshold noise reduction module to construct a convolutional network in front of the transformer structure. This configuration extracts fault feature information and removes the noise, enhancing the fault recognition accuracy of the model. Experimental results on three diverse datasets demonstrate that DRSwin-ST exhibits robustness and high accuracy even in scenarios with limited samples and high noise, validating its exceptional performance.

Lightweight object detection in low light: Pixel-wise depth refinement and TensorRT optimization

Images captured in low-light conditions present significant challenges for accurate object detection due to factors such as high noise, poor illumination, and low contrast. In this study, we propose an innovative approach that combines pixel-wise depth refinement and TensorRT optimization to improve object detection performance and inference speed under challenging lighting scenarios. The Pixel-wise depth refinement utilizes a deep learning model to estimate the depth curve for each pixel in the low-light image, providing detailed scene depth information. We used the Lightweight YOLOv8 object detection model using TensorRT. This will reduce the model's computational complexity and memory footprint, enabling faster inference and real-time performance on resource-constrained devices. By combining these techniques, our approach aims to accomplish enhanced entity recognition precision in reduced-illumination environments while maintaining efficiency and real-time performance. We evaluate the effectiveness of our proposed modifications to the depth curve estimation block and the overall approach on benchmark datasets and demonstrate its potential for enhancing object detection capabilities in challenging low-light conditions. Furthermore, the experimental results show an improvement in the precision, recall, and mean average precisions (mAP), and also by using the TensorRT the inference speed is four times faster than the existing one.

Segmentation of 3D Point Clouds of Heritage Buildings Using Edge Detection and Supervoxel-Based Topology.

This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topological structure generated from the supervoxelization of the 3D point clouds, which is used to make the closure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrated the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings.

Influence of uncomfortable environmental conditions on odor detection thresholds for n-butanol determined with a dynamic dilution olfactometer and in room air

Odor guide values (OGV) are used when a person is exposed to an odor in a room and complains about odor annoyance. OGV are derived from odor detection thresholds (ODT) measured under standard environmental conditions. The study question was whether uncomfortable environmental conditions alter odor perception and should therefore be considered when applying OGV. ODT for n-butanol were determined with an olfactometer and in room air. Twenty healthy, non-smoking volunteers (10 women/10 men, 19–50 years) were selected and trained compliant with the European Standard for Olfactometry EN 13725:2022. Standard conditions were an average temperature between 22 °C and 23 °C, warm light (2800 Kelvin), quiet fan noise (45 dB), 415 ppm carbon dioxide (CO2), and relative humidity between 34 % and 42 %. On each test day, one of five conditions was examined: high temperature (26°C), cold light (6500 Kelvin), traffic noise (70 dB with peaks up to 85 dB), 1000 ppm CO2 and 4000 ppm CO2. Comparability of log-transformed ODT values was assessed by Bland-Altman plot analysis. None of the five conditions systematically affected ODT, either when measured with an olfactometer or in room air. Compared to standard conditions (Limits of Agreement (± LoA) 1.03), the variability of ODT was higher at high temperature (± LoA 1.70) and traffic noise (± LoA 1.45), but not at cold light (± LoA 1.02), 1000 ppm CO2 (± LoA 0.87) or 4000 ppm CO2 (± LoA 0.93). The results show that OGV can be used in uncomfortable environmental conditions. However, because ODT were lower or higher in a few subjects at high temperatures and traffic noise, individual occupant’s perception of temperature and noise should always be considered when applying the OGV concept. Furthermore, the results confirm that the experimental determination of ODT should be performed under controlled and standardized environmental conditions.

3D wave dispersion analysis of graphene platelet-reinforced ultra-stiff double functionally graded nanocomposite sandwich plates with metamaterial honeycomb core layer

This research addresses the three-dimensional thermomechanical wave propagation behavior in sandwich composite nanoplates with a metamaterial honeycomb core layer and double functionally graded (FG) ultra-stiff surface layers. Due to its potential for high-temperature applications, pure nickel (Ni) is preferred for the honeycomb core layer, and an Al2O3/Ni ceramic-metal matrix is preferred for the surface layers. The functional distribution of graphene platelets (GPLs) in three different patterns, Type-U, Type-X, and Type-O, in the metal-ceramic matrix with a power law distribution provides double-FG properties to the surface layers. The mechanical and thermal material characteristics of the core and surface layers, as well as the reinforcing GPLs, are temperature-dependent. The pattern of temperature variation over the plate thickness is considered to be nonlinear. The sandwich nanoplate’s motion equations are obtained by combining the sinusoidal higher-order shear deformation theory (SHSDT) with nonlocal integral elasticity and strain gradient elasticity theories. The wave equations are established by using Hamilton’s principle. Parametric simulations and graphical representations are performed to analyze the effects of honeycomb size variables, wave number, the power law index, the GPL distribution pattern, the GPL weight ratio, and the temperature rise on three-dimensional wave propagation in an ultra-stiff sandwich plate. The results of the analysis reveal that the 3D wave propagation of the sandwich nanoplate can be significantly modified or tuned depending on the desired parameters and conditions. Thus, the proposed sandwich structure is expected to provide essential contributions to radar/sonar stealth applications in air, space, and submarine vehicles in high or low-temperature environments, protection of microelectromechanical devices from high noise and vibration, soft robotics applications, and wearable health and protective equipment applications.

Inverse design of growth-inspired irregular architected materials for programmable properties

Biomimetic metamaterials have gained increasing attention due to their exceptional characteristics such as high toughness, robust strength, and effective noise reduction. However, their complex and irregular nature presents challenges in tailoring their mechanical properties for specific applications. This study proposes a novel dual-network approach to overcome these challenges. The approach involves creating a forward model to accurately predict the mechanical properties and interconnectivity of the metamaterial without the need for growth and homogenization processes. Additionally, an inverse model is utilized to accurately predict designs for desired anisotropic stiffness. Compared to traditional bidirectional networks, our approach demonstrates superior accuracy in designing elastic properties. Our results also show that the metamaterial exhibits a broad low-frequency response while maintaining exceptional load-carrying capacity, making it a promising solution for designing low-frequency vibration suppression metamaterials.

Terahertz Radiation Detectors Using CMOS Compatible SOI Substrates

AbstractIn recent years, silicon‐based room temperature Terahertz (THz) detectors have become the most optimistic research area because of their high speed, low cost, and unimpeded compatibility with mainstream complementary metal‐oxide‐semiconductor (CMOS) device technologies. However, Silicon (Si) suffers from low responsivity and high noise at THz frequencies. In this review, the recent advances in Si‐based THz detectors using silicon‐on‐insulator (SOI) substrates are presented. These offer several advantages over bulk counterparts, such as reduced parasitic capacitance, enhanced electric field confinement, and improved thermal isolation. The different types of THz detectors exploiting SOI substrate, such as conventional metal‐oxide‐semiconductor field effect transistors (MOSFETs), junction‐less MOSFETs, junction‐less nanowires field effect transistors (JLNWFETs), micro‐electromechanical system (MEMS), metal‐semiconductor‐metal (MSM) structures, and single electron transistor (SET), are discussed, and their key performances in terms of responsivity, noise equivalent power (NEP), bandwidth, and dynamic range are compared. The challenges and opportunities for further improvement of SOI THz detectors, such as device scaling, integration, and modulation, are also highlighted. This review may offer compelling evidence supporting the idea that SOI THz detectors have the potential to facilitate high performance, low power consumption, and scalability—qualities essential for advancing next‐level technologies.

A Practical Approach to Tracking Estimation Using Object Trajectory Linearization

In the field of image processing and machine vision, object tracking is a significant and rapidly developing subfield. The numerous potential applications of object tracking have garnered much attention in recent years. The effectiveness of tracking and detecting moving targets is directly related to the quality of motion detection algorithms. This paper presents a new method for estimating the tracking of objects by linearizing their trajectories. Estimating the movement paths of objects in dynamic and complex environments is one of the fundamental challenges in various fields, such as surveillance systems, autonomous navigation, and robotics. Existing methods, such as the Kalman filter and particle filter, each have their strengths and weaknesses. The Kalman filter is suitable for linear systems but less efficient in nonlinear systems, while the particle filter can better handle system nonlinearity but requires more computations. The main goal of this research is to improve the accuracy and efficiency of estimating the movement paths of objects by combining path linearization techniques with existing advanced methods. In this method, the nonlinear model of the object's path is first transformed into a simpler linear model using linearization techniques. The Kalman filter is then used to estimate the states of the linearized system. This approach simplifies the calculations while increasing the estimation accuracy. In the subsequent step, a particle filter-based method is employed to manage noise and sudden changes in the object's trajectory. This combination of two different methods allows leveraging the advantages of both, resulting in a more accurate and robust estimate. Experimental results show that the proposed method performs better than traditional methods, achieving higher accuracy in various conditions, including those with high noise and sudden changes in the movement path. Specifically, the proposed approach improves movement forecasting accuracy by about 12% compared to existing methods. In conclusion, this research demonstrates that object trajectory linearization can be an effective tool for improving object tracking estimation. Combining this technique with existing advanced methods can enhance the accuracy and efficiency of tracking systems. Consequently, the results of this research can be applied to the development of advanced surveillance systems, self-driving cars, and other applications.

Neighborhood repartition-based oversampling algorithm for multiclass imbalanced data with label noise

The classification of imbalanced data remains one of the most significant topics in contemporary data analysis. Existing classification algorithms tend to favor majority classes, leading to false predictions and difficulties in addressing class overlap or label noise. These challenges are particularly evident in multiclass settings, where the mutual imbalance relationships among classes become more complex. Despite this, the vast majority of research in this field has concentrated on binary problems, while the more difficult multiclass problems are relatively underexplored. In this paper, we propose a novel data-sampling technique, a Multiclass Neighborhood Repartition-based Oversampling (MC-NRO) algorithm. The innovation of this method lies in it considers local data characteristics of each class to constrain the oversampled neighborhood. MC-NRO calculates the mutual potential of different classes to precisely optimize the subregions for generating new instances. By selecting different repartition neighborhoods to meet the needs of specific domain, it can detect outliers and label noise, expand the decision boundary of minority class, and avoid class overlap through data cleaning. The experimental results demonstrate that MC-NRO outperforms other advanced oversampling strategies, ranking first on average across the three evaluation metrics, and exhibits robustness, especially in datasets with high noise levels. More importantly, MC-NRO is highly versatile and can be flexibly applied to various classifiers, and is particularly suitable for processing naturally complex (i.e., not affected by noise) datasets.

DESIGN AND EVALUATION OF PIEZOELECTRIC-BASED PASSIVE DAMPING FOR HIGH FREQUENCY NOISE SUPPRESSION

This study focuses on developing a piezoelectric-based passive damper to mitigate high- frequency brake noise. The research investigates different designs of piezoelectric transducer elements to convert mechanical energy into electrical energy using a mass sliding belt test system that simulates real disc brake noise mechanisms. Integration of these electrical elements into the existing mass-sliding belt system is investigated. Five distinct piezoelectric transducer designs (Design-1, Design-2, Design-3, Design-4 and Design-5), each with unique technical specifications, boundary conditions, and scaling, are developed to suppress high frequency noise from friction-induced vibrations. To facilitate effective selection, a scoring method is employed, identifying Design-5 with a cylindrical model as the most efficient transducer design. Evaluation criteria include energy generation, compressive strength, operating temperature, weight, ergonomic usability, cost, and safety. Analyses are performed to assess potential stress-induced damage to the piezoelectric material resulting from the selected design. This study aims to contribute a novel perspective to noise reduction techniques and successfully demonstrates the integration of a piezoelectric transducer and an RLC circuit into a functional system.

Enhancing vascular network visualization in 3D photoacoustic imaging: in vivo experiments with a vasculature filter

Filter-based vessel enhancement algorithms facilitate the extraction of vascular networks from medical images. Traditional filter-based algorithms struggle with high noise levels in images with false vessel extraction, and a low standard deviation (σ) value may introduce gaps at the centers of wide vessels. In this paper, a robust technique with less sensitivity to parameter tuning and better noise suppression than other filter-based methods for two-dimensional and three-dimensional images is implemented. In this study, we propose a filter that employs non-local means (NLM) for denoising, applying the vesselness function to suppress blob-like structures and filling the gaps in wide vessels without compromising edge quality or details. Acoustic resolution photoacoustic microscopy (AR-PAM) systems generate high-resolution volumetric photoacoustic images, but their vascular structure imaging suffers from out-of-focal signal-to-noise ratio (SNR) and lateral resolution loss. Implementing a synthetic aperture focusing technique (SAFT) based on a virtual detector (VD) improves out-of-focal region resolution and SNR. Combining the proposed filter with the SAFT algorithm enhances vascular structural imaging in AR-PAM systems. The proposed method is robust and applicable for animal tissues with less error of vasculature structure extraction in comparison to traditional fliter-based methods like Frangi and Sato filter. Also, the method is faster in terms of processing speed and less tuning parameters. We applied the method to a digital phantom to validate our approach and conducted in vivo experiments to demonstrate its superiority for real volumetric tissue imaging.