Convolution Algorithm Research Articles

A Nonlinear Convolutional Neural Network Algorithm for Autonomous Vehicle Lane Line Detection

A Nonlinear Convolutional Neural Network Algorithm for Autonomous Vehicle Lane Line Detection

Complementary testing and machine learning techniques for the characterization and prediction of middle Permian tight gas sandstone reservoir quality in the northeastern Ordos Basin, China

In this study, an integrated approach for diagenetic facies classification, reservoir quality analysis and quantitative wireline log prediction of tight gas sandstones (TGSs) is introduced utilizing a combination of fit-for-purpose complementary testing and machine learning techniques. The integrated approach is specialized for the middle Permian Shihezi Formation TGSs in the northeastern Ordos Basin, where operators often face significant drilling uncertainty and increased exploration risks due to low porosities and micro-Darcy range permeabilities. In this study, detrital compositions and diagenetic minerals and their pore type assemblages were analyzed using optical light microscopy, cathodoluminescence, standard scanning electron microscopy, and X-ray diffraction. Different types of diagenetic facies were delineated on this basis to capture the characteristic rock properties of the TGSs in the target formation. A combination of He porosity and permeability measurements, mercury intrusion capillary pressure and nuclear magnetic resonance data was used to analyze the mechanism of heterogeneous TGS reservoirs. We found that the type, size and proportion of pores considerably varied between diagenetic facies due to differences in the initial depositional attributes and subsequent diagenetic alterations; these differences affected the size, distribution and connectivity of the pore network and varied the reservoir quality. Five types of diagenetic facies were classified: (ⅰ) grain-coating facies, which have minimal ductile grains, chlorite coatings that inhibit quartz overgrowths, large intergranular pores that dominate the pore network, the best pore structure and the greatest reservoir quality; (ⅱ) quartz-cemented facies, which exhibit strong quartz overgrowths, intergranular porosity and a pore size decrease, resulting in the deterioration of the pore structure and reservoir quality; (ⅲ) mixed-cemented facies, in which the cementation of various authigenic minerals increases the micropores, resulting in a poor pore structure and reservoir quality; (ⅳ) carbonate-cemented facies and (ⅴ) tightly compacted facies, in which the intergranular pores are filled with carbonate cement and ductile grains; thus, the pore network mainly consists of micropores with small pore throat sizes, and the pore structure and reservoir quality are the worst. The grain-coating facies with the best reservoir properties are more likely to have high gas productivity and are the primary targets for exploration and development. The diagenetic facies were then translated into wireline log expressions (conventional and NMR logging). Finally, a wireline log quantitative prediction model of TGSs using convolutional neural network machine learning algorithms was established to successfully classify the different diagenetic facies.

An overview of hand gesture recognition based on computer vision

Hand gesture recognition emerges as one of the foremost sectors which has gone through several developments within pattern recognition. Numerous studies and research endeavors have explored methodologies grounded in computer vision within this domain. Despite extensive research endeavors, there is still a need for a more thorough evaluation of the efficiency of various methods in different environments along with the challenges encountered during the application of these methods. The focal point of this paper is the comparison of different research in the domain of vision-based hand gesture recognition. The objective is to find out the most prominent methods by reviewing efficiency. Concurrently, the paper delves into presenting potential solutions for challenges faced in different research. A comparative analysis particularly centered around traditional methods and convolutional neural networks like random forest, long short-term memory (LSTM), heatmap, and you only look once (YOLO). considering their efficacy. Where convolutional neural network-based algorithms performed best for recognizing the gestures and gave effective solutions for the challenges faced by the researchers. In essence, the findings of this review paper aim to contribute to future implementations and the discovery of more efficient approaches in the gesture recognition sector.

Research on a Train Safety Driving Method Based on Fusion of an Incremental Clustering Algorithm and Lightweight Shared Convolution.

This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as a core method, combined with dilation and erosion theories, to expand the boundaries of point cloud clusters, merging adjacent point cloud elements into unified clusters. This method effectively identifies and connects spatially adjacent point cloud clusters while efficiently eliminating noise from target object point clouds, thereby achieving more precise recognition of unknown obstacles on the track. Furthermore, the effective integration of this algorithm with lightweight shared convolutional semantic segmentation algorithms enables accurate localization of obstacles. Experimental results using two combined public datasets demonstrate that the obstacle detection average recall rate of the proposed method reaches 90.3%, significantly enhancing system reliability. These findings indicate that the proposed detection strategy effectively improves the accuracy and real-time performance of obstacle recognition, thereby presenting important practical application value for ensuring the safe operation of railway tracks.

Implementation of Convolutional Neural Network Algorithms to Detect the Ripeness of Palm Fruits based on Image Colors

The ripeness of oil palm fruit greatly affects the quality of the oil produced. Usually, humans manually detect the maturity of oil palm fruit, a process that can be inconsistent and time-consuming. Therefore, there is a need for automated methods that can improve accuracy and efficiency in determining the maturity of oil palm fruit. The study proposes using the Convolutional Neural Network algorithm to detect oil palm fruit maturity based on digital color images. We expect a convolutional neural network, known for its effectiveness in image analysis, to deliver accurate results in classifying fruit maturity. The aim of this study is to develop and test a model of a convolutional neural network that can classify oil palm fruit into three maturity categories: raw, ripe, and rotten. This study employs a maturity-categorized data set of oil palm images. We built and trained a convolutional neural network model using this data set. We evaluate the model performance using four main metrics: accuracy, precision, recall, and f1-score. We perform an in-depth analysis to assess model performance in each maturity category. The confusion matrix yielded an accuracy of 74.6%. The convolutional neural network model developed showed the highest precision results for the ripe fruit (86.00%), followed by rotten fruit (72.30%), and raw fruit (67.30%). We obtained the highest recalls (85.40%) for the raw fruit category, followed by ripe fruits (75.50%) and rotten fruits (72.30%.

A knowledge graph algorithm enabled deep recommendation system.

Personalized learning resource recommendations may help resolve the difficulties of online education that include learning mazes and information overload. However, existing personalized learning resource recommendation algorithms have shortcomings such as low accuracy and low efficiency. This study proposes a deep recommendation system algorithm based on a knowledge graph (D-KGR) that includes four data processing units. These units are the recommendation unit (RS unit), the knowledge graph feature representation unit (KGE unit), the cross compression unit (CC unit), and the feature extraction unit (FE unit). This model integrates technologies including the knowledge graph, deep learning, neural network, and data mining. It introduces cross compression in the feature learning process of the knowledge graph and predicts user attributes. Multimodal technology is used to optimize the process of project attribute processing; text type attributes, multivalued type attributes, and other type attributes are processed separately to reconstruct the knowledge graph. A convolutional neural network algorithm is introduced in the reconstruction process to optimize the data feature qualities. Experimental analysis was conducted from two aspects of algorithm efficiency and accuracy, and the particle swarm optimization, neural network, and knowledge graph algorithms were compared. Several tests showed that the deep recommendation system algorithm had obvious advantages when the number of learning resources and users exceeded 1,000. It has the ability to integrate systems such as the particle swarm optimization iterative classification, neural network intelligent simulation, and low resource consumption. It can quickly process massive amounts of information data, reduce algorithm complexity and requires less time and had lower costs. Our algorithm also has better efficiency and accuracy.

Research on image saliency detection based on deep neural network

AbstractAs a hot research field at present, computer vision is devoted to the rapid acquisition and application of target information from images or videos by simulating human visual mechanism. In order to improve the accuracy and efficiency of image detection, image saliency region detection technology has received more and more attention in the field of computer vision research; an important research content in the field, the core part of which lies in the research on algorithms related to feature extraction and saliency calculation of targets. This paper analyzes the multi‐feature fusion saliency detection model and visual saliency calculation process, and based on the existing algorithm, by improving the VGG16 network, a fully convolutional network saliency detection algorithm is proposed. The qualitative and quantitative experimental results show that compared with the four mainstream methods of BL, GS, SF, and RFCN, our algorithm not only improves the accuracy of salient object detection, but also effectively solves the problem of target edge blur. Therefore, this study has improved the accuracy and efficiency of saliency detection, which can not only promote the development of computer vision technology, but also provide support for research in the field of image processing.

Classification and Severity Level Assessment of Fusarium Wilt Disease in Chickpeas using Convolutional Neural Network

Background: The fusarium wilt disease of chickpea leaves is a common illness that leads to economic problems for farmers due decreased crop yield. Early disease detection and the implementation of suitable precautions can help to increase the yield of chickpeas. This study offers an improved method for Fusarium wilt disease prediction based on severity level using a convolutional neural learning algorithm. Methods: The Convolutional Neural Network (CNN) model is utilized in this work to identify leaf disease due to wilting. The dataset contains 4,339 images of chickpea leaves that were obtained from Kaggle. After preprocessing, the data is sent into the network model for training. The model shows acceptable classification and accuracy metrics. Result: Deep learning methods are very useful tools for tracking leaf diseases at their early stages and can help farmers with the use of controlling methods. The proposed work looks for changes in the shape and color of chickpea leaves in order to predict severe fusarium disease. Training and validation accuracies show a balanced trade-off by giving satisfactory outcomes. The model shows an overall accuracy of 74.79%. The confusion matrix and classification parameters increase the model’s performance.

Quantifying the effect of pulse-like ground motions on the seismic design of SMA restrained isolation bridges using probabilistic repair cost method

The shape memory alloy (SMA) restrainer serves as an effective but expensive bridge restraining device. However, the impact of pulse effect on SMA design approach under pulse-like ground motions (PLGM) has not been fully quantified in previous studies, which hinders its application in near-fault regions. Therefore, based on the risk probability assessment method throughout the entire life cycle, by accounting for the comprehensive repair cost of each component damage, this paper introduces a parameter design method for SMA restrainer of near-fault bridges that considers structural parameters, pulse parameters, and economic indicators. Firstly, the repair cost ratio (RCR) of bridge system, which means the expenses for repairs expressed as a proportion of bridge replacement costs, was regarded as the life-time optimization goal and overall performance indicator. Secondly, by accounting for near-fault effects, the relationship between RCR and SMA design parameters was established by convolution algorithm of probabilistic seismic hazard analysis, demand analysis and capacity analysis. A novel probabilistic seismic demand model was utilized to quickly determine the RCR of the bridge system under PLGM. Finally, the influence of pulse period on the rational design parameter of SMA restrainer was comprehensively investigated by RCR-based method. A seismic isolation arch bridge was selected as the illustrated case in this article. The results indicate that the rational design parameters of SMA exhibit a pattern of initially rising, then falling as the pulse period increases, reaching the peak value when the pulse period approaches bridge fundamental period. Moreover, the price parameter of SMA significantly affect the optimal design parameters, and the effective range is also recommended.

Practical Application and Case Analysis of Computer Image Vision Technology in Music Education and Teaching

Exploring how to utilize images to enrich music teaching content and provide a more visually impactful learning experience is an important topic. Therefore, this paper introduces a convolutional neural network-based algorithm for extracting audio features to construct a music visualization model. By identifying features such as note pitches, it enhances pitch recognition and integrates with CNN algorithms for audio information visualization. Experimental results demonstrate an accuracy rate exceeding 97%, showcasing the significant advantage of this method in visualizing audio information in music multimedia classrooms. It provides technical support for bringing a new visual experience to music education.

Study on Deep-Learning Model for Phase Resolved Partial Discharge Pattern Classification Based on Convolutional Neural Network Algorithm

Study on Deep-Learning Model for Phase Resolved Partial Discharge Pattern Classification Based on Convolutional Neural Network Algorithm

Development of a monthly PM2.5 forecast model for Seoul, Korea, based on the dynamic climate forecast and a convolutional neural network algorithm

A convolutional neural network (CNN) model has been developed to predict monthly concentrations of particulate matter with a diameter ≤ 2.5 μm (PM2.5) in Seoul, Republic of Korea, during winter months (November through February). Seven meteorological variables influencing PM2.5 concentrations were selected as predictors: geopotential heights at 1000 and 500 hPa, zonal and meridional winds, relative humidity and temperature at 850 hPa, and boundary layer height. These predictors were obtained from the fifth generation of the European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) for model construction and from the Korea Polar Prediction System (KPOPS) dynamic forecasts for operational predictions. From the 11-year ERA5 dataset for 2008–2020, one year was allocated for testing, while the remaining 10 years were randomly assigned for either training (80%) or validation (20%). This process was repeated until all 11 years were allocated for testing, resulting in 11 CNN models. The benchmark which used the ERA5 dataset as the predictors yielded a mean bias error (MBE) of 0.05 μg m−3, root mean square error (RMSE) of 2.41 μg m−3, Pearson correlation coefficient (R) of 0.85, and 71% hit rate in predicting the months of high PM2.5 concentrations (≥ 30 μg m−3) for the testing periods. The 11-CNN-model ensemble predictions using the KPOPS forecasts resulted in an MBE of 0.1 μg m−3, RMSE of 3.19 μg m−3, R of 0.74, and hit rate of 62.5% for the period 2008–2020. Considering that the average temporal correlation coefficients between the ERA5 and the KPOPS forecasts for the seven predictors ranged from 0.20 to 0.66, the CNN-based PM2.5 prediction model has demonstrated good performance in overcoming the adverse effects of the KPOPS forecast errors on predicting PM2.5 concentrations. The CNN model showed limitations for unusual period such as the COVID-19 period, November 2020 through February 2022, when the emissions in East Asia were substantially below their climatological normal levels. In conclusion, the CNN model developed in this study showed potential applicability to operational monthly PM2.5 concentration predictions for the years of normal emissions using solely meteorological data. This can provide professionals in various fields, such as health care and environmental science, with valuable insights for long-term planning.

Comparative Evaluation of Convolutional Neural Network Object Detection Algorithms for Vehicle Detection.

The domain of object detection was revolutionized with the introduction of Convolutional Neural Networks (CNNs) in the field of computer vision. This article aims to explore the architectural intricacies, methodological differences, and performance characteristics of three CNN-based object detection algorithms, namely Faster Region-Based Convolutional Network (R-CNN), You Only Look Once v3 (YOLO), and Single Shot MultiBox Detector (SSD) in the specific domain application of vehicle detection. The findings of this study indicate that the SSD object detection algorithm outperforms the other approaches in terms of both performance and processing speeds. The Faster R-CNN approach detected objects in images with an average speed of 5.1 s, achieving a mean average precision of 0.76 and an average loss of 0.467. YOLO v3 detected objects with an average speed of 1.16 s, achieving a mean average precision of 0.81 with an average loss of 1.183. In contrast, SSD detected objects with an average speed of 0.5 s, exhibiting the highest mean average precision of 0.92 despite having a higher average loss of 2.625. Notably, all three object detectors achieved an accuracy exceeding 99%.

Deep Learning and Face Recognition: Face Recognition Approach Based on the DS-CDCN Algorithm

To enhance the performance and reliability of the face recognition algorithm that is based on deep learning technology, this study utilizes a density-based noise-applied spatial clustering algorithm to cluster a large-scale face image dataset, resulting in a self-constructed dataset. A deep separable center differential convolutional network algorithm is utilized for face recognition. The impact of convolutional parameters on the algorithm’s performance is verified through experiments with ablated convolutional parameters. The study found that the density-based noise-applied spatial clustering algorithm resulted in time savings of 43.66% and 51.22% compared to the K-means clustering algorithm and the hierarchical clustering algorithm, respectively, when analyzing 8000 images. Additionally, the depth-separable center difference convolutional network algorithm had a lower average classification error rate compared to the other two algorithms, with reductions of 2.49% and 17.01%, respectively. The depth-separable center difference convolutional network technique is an advanced method for identifying the faces of people of different races, according to the experimental investigation. It can provide efficient and accurate services for the face recognition needs of various races.

Improving Earth-like planet detection in radial velocity using deep learning

Context. Many novel methods have been proposed to mitigate stellar activity for exoplanet detection as the presence of stellar activity in radial velocity (RV) measurements is the current major limitation. Unlike traditional methods that model stellar activity in the RV domain, more methods are moving in the direction of disentangling stellar activity at the spectral level. As deep neural networks have already been proven to be one of the most effective tools in data mining, in this work, we explore their potential in the context of Earth-like planet detection in RV measurements. Aims. The goal of this paper is to present a novel convolutional neural network-based algorithm that efficiently models stellar activity signals at the spectral level, enhancing the detection of Earth-like planets. Methods. Based on the idea that the presence of planets can only produce a Doppler shift at the spectral level while the presence of stellar activity can introduce a variation in the profile of spectral lines (asymmetry and depth change), we trained a convolutional neural network to build the correlation between the change in the spectral line profile and the corresponding RV, full width at half maximum (FWHM) and bisector span (BIS) values derived from the classical cross-correlation function. Results. This algorithm has been tested on three intensively observed stars: Alpha Centauri B (HD 128621), Tau ceti (HD 10700), and the Sun. By injecting simulated planetary signals at the spectral level, we demonstrate that our machine learning algorithm can achieve, for HD 128621 and HD 10700, a detection threshold of 0.5 m s−1 in semi-amplitude for planets with periods ranging from 10 to 300 days. This threshold would correspond to the detection of a ~4 M⊕ in the habitable zone of those stars. On the HARPS-N solar dataset, thanks to significantly more data, our algorithm is even more efficient at mitigating stellar activity signals and can reach a threshold of 0.2 m s−1, which would correspond to a 2.2 M⊕ planet on the orbit of the Earth. Conclusions. To the best of our knowledge, it is the first time that such low detection thresholds are reported for the Sun, but also for other stars, and therefore this highlights the efficiency of our convolutional neural network-based algorithm at mitigating stellar activity in RV measurements.

Detection of Cervical Foraminal Stenosis from Oblique Radiograph Using Convolutional Neural Network Algorithm.

This study was conducted to develop a convolutional neural network (CNN) algorithm that can diagnose cervical foraminal stenosis using oblique radiographs and evaluate its accuracy. A total of 997 patients who underwent cervical MRI and cervical oblique radiographs within a 3-month interval were included. Oblique radiographs were labeled as "foraminal stenosis" or "no foraminal stenosis" according to whether foraminal stenosis was present in the C2-T1 levels based on MRI evaluation as ground truth. The CNN model involved data augmentation, image preprocessing, and transfer learning using DenseNet161. Visualization of the location of the CNN model was performed using gradient-weight class activation mapping (Grad-CAM). The area under the curve (AUC) of the receiver operating characteristic curve based on DenseNet161 was 0.889 (95% confidence interval, 0.851-0.927). The F1 score, accuracy, precision, and recall were 88.5%, 84.6%, 88.1%, and 88.5%, respectively. The accuracy of the proposed CNN model was significantly higher than that of two orthopedic surgeons (64.0%, p<0.001; 58.0%, p<0.001). Grad-CAM analysis demonstrated that the CNN model most frequently focused on the foramen location for the determination of foraminal stenosis, although disc space was also frequently taken into consideration. A CNN algorithm that can detect neural foraminal stenosis in cervical oblique radiographs was developed. The AUC, F1 score, and accuracy were 0.889, 88.5%, and 84.6%, respectively. With the current CNN model, cervical oblique radiography could be a more effective screening tool for neural foraminal stenosis.

Graph convolutional LSTM algorithm for real-time crash prediction on mountainous freeways

Accurate real-time traffic crash prediction is crucial for proactive traffic safety management. Currently, the majority of real-time models forecast crashes every 5 min to support different intelligent transportation systems. However, these intervals might be too short for practical use in manually implementing proactive traffic safety measures such as deploying traffic law enforcement and emergency rescue resources. Therefore, this study develops hourly crash prediction models to provide network operators with sufficient time to take measures in advance. A section of a mountainous freeway in Guizhou province is divided into homogeneous segments, with crash data, traffic operations data, and meteorological data being collected hourly. As the result is an imbalanced dataset of crash and non-crash instances, the training dataset is resampled using synthetic minority over-sampling technique (SMOTE) to address the issue. To fully capture the complex spatiotemporal relationships in the data and achieve high crash prediction accuracy, a graph convolutional network-long short-term memory (GCN-LSTM) model is constructed for the first time, combining a graph convolutional network (GCN) and long short-term memory (LSTM) neural network. For comparison purposes, LSTM, Extreme Gradient Boosting (XGBoost), and logistic regression (LR) models are developed. The results show that the GCN-LSTM model outperforms other models in hourly traffic crash prediction, and the optimal prediction performance is achieved with the crash-to-non-crash ratio of 1:4. The GCN-LSTM method is found to effectively capture the complex spatiotemporal relationships in prediction data and to handle imbalanced traffic crash data.

The impact of plan complexity on calculation and measurement-based pre-treatment verifications for sliding-window intensity-modulated radiotherapy

Background and purposeIn sliding-window intensity-modulated radiotherapy, increased plan modulation often leads to increased plan complexities and dose uncertainties. Dose calculation and/or measurement checks are usually adopted for pre-treatment verification. This study aims to evaluate the relationship among plan complexities, calculated doses and measured doses. Materials and methodsA total of 53 plan complexity metrics (PCMs) were selected, emphasizing small field characteristics and leaf speed/acceleration. Doses were retrieved from two beam-matched treatment devices. The intended dose was computed employing the Anisotropic Analytical Algorithm and validated through Monte Carlo (MC) and Collapsed Cone Convolution (CCC) algorithms. To measure the delivered dose, 3D diode arrays of various geometries, encompassing helical, cross, and oblique cross shapes, were utilized. Their interrelation was assessed via Spearman correlation analysis and principal component linear regression (PCR). ResultsThe correlation coefficients between calculation-based (CQA) and measurement-based verification quality assurance (MQA) were below 0.53. Most PCMs showed higher correlation rpcm-QA with CQA (max: 0.84) than MQA (max: 0.65). The proportion of rpcm-QA ≥ 0.5 was the largest in the pelvis compared to head-and-neck and chest-and-abdomen, and the highest rpcm-QA occurred at 1 %/1mm. Some modulation indices for the MLC speed and acceleration were significantly correlated with CQA and MQA. PCR’s determination coefficients (R2) indicated PCMs had higher accuracy in predicting CQA (max: 0.75) than MQA (max: 0.42). ConclusionsCQA and MQA demonstrated a weak correlation. Compared to MQA, CQA exhibited a stronger correlation with PCMs. Certain PCMs related to MLC movement effectively indicated variations in both quality assurances.

Generalized Quantum Signal Processing

Quantum signal processing (QSP) and quantum singular value transformation (QSVT) currently stand as the most efficient techniques for implementing functions of block-encoded matrices, a central task that lies at the heart of most prominent quantum algorithms. However, current QSP approaches face several challenges, such as the restrictions imposed on the family of achievable polynomials and the difficulty of calculating the required phase angles for specific transformations. In this paper, we present a generalized quantum signal processing (GQSP) approach, employing general SU(2) rotations as our signal-processing operators, rather than relying solely on rotations in a single basis. Our approach lifts all practical restrictions on the family of achievable transformations, with the sole remaining condition being that |P|≤1, a restriction necessary due to the unitary nature of quantum computation. Furthermore, GQSP provides a straightforward recursive formula for determining the rotation angles needed to construct the polynomials in cases where P and Q are known. In cases where only P is known, we provide an efficient optimization algorithm capable of identifying in under a minute of GPU time, a corresponding Q for polynomials of degree on the order of 107. We further illustrate GQSP simplifies QSP-based strategies for Hamiltonian simulation, offer an optimal solution to the ϵ-approximate fractional query problem that requires O((1/δ)+log(1/ϵ)) queries to perform where O(1/δ) is a proved lower bound, and introduces novel approaches for implementing bosonic operators. Moreover, we propose a novel framework for the implementation of normal matrices, demonstrating its applicability through synthesis of diagonal matrices, as well as the development of a new algorithm for convolution through synthesis of circulant matrices using only O(dlogN+log2N) 1 and 2-qubit gates for a filter of lengths d. Published by the American Physical Society 2024

Coherence-based phase aberration correction and beamforming for ring-array ultrasound imaging

Degradation of image quality caused by aberrations and off-axis interferences is common in medical ultrasound imaging. The ring-array echo imaging also suffers from these constraints, where phase aberrations caused by tissue heterogeneity create overlapping of interest target and worsen the interpretability of anatomical structures; while off-axis interferences reduce the spatial resolution and contrast of the reconstructed images. To address them, the signal coherence is available since they can be considered as incoherent interference and noise. The coherence factor (CF) is sensitive to phase aberrations and is thus adopted in this study. Moreover, the convolutional beamforming algorithm (COBA) specifically exploits the autocorrelation operation to improve the signal coherence and effectively reduce noise. Therefore, we first utilize the CF to detect the local coherence and its maximization criterion as a way to estimate the sound speed to correct for phase aberration. Then, CF is used as an adaptive weighting factor to suppress the noise, and based on this, we develop a short-lag COBA to further enhance the image quality, referred to as the CF_SLCOBA method. Simulations and experiments were performed to evaluate the performance of the proposed methods. Results show that, the improvements of the CF_SLCOBA method in resolution is about 26.8% compared with CF; while about 92.0% in contrast ratio (CR) compared with COBA, in the experiments. Meanwhile, the proposed method yields a good performance in phase aberration correction. This demonstrates that the proposed method may provide benefits for rebuilding high-quality images, which also reveals that it offers a certain potential for practical applications.