Noise Information Research Articles

A skeleton extraction method for large-scale spatial interaction networks considering spatial distribution characteristics

Constructing spatial interaction (SI) networks from massive geographic flow data is an important method for understanding the correlation patterns and trends among regions. However, large-scale SI networks usually cause complex edge interleaving and a sharp increase in noise information. A network skeleton can represent the key structure of the corresponding complex network with generalized network data. However, the existing extraction methods only consider the topological information of networks and ignore spatial information, which may cause difficulty maintaining the spatial distribution characteristics and the exclusion of key spatial information. To address the issue, we have developed a skeleton extraction method for large-scale SI networks. First, the network nodes are divided into different clusters via adaptive spatial clustering. Second, an entropy weight method is used to define a comprehensive evaluation indicator of the network node importance, and the nodes in each cluster are sorted and selected according to the comprehensive evaluation indicator. Finally, the skeleton structure of the SI network is reconstructed according to the selected nodes and their adjacent edges. Compared with the existing methods for extracting network skeletons, the proposed method can maintain the spatial distribution characteristics of SI networks while preserving key topological features.

RFCS-YOLO: Target Detection Algorithm in Adverse Weather Conditions via Receptive Field Enhancement and Cross-Scale Fusion

The paper proposes a model based on receptive field enhancement and cross-scale fusion (RFCS-YOLO). It addresses challenges like complex backgrounds and problems of missing and mis-detecting traffic targets in bad weather. First, an efficient feature extraction module (EFEM) is created. It reconfigures the backbone network. This helps to make the receptive field better and improves its ability to extract features of targets at different scales. Next, a cross-scale fusion module (CSF) is introduced. It uses the receptive field coordinate attention mechanism (RFCA) to fuse information from different scales well. It also filters out noise and background information that might interfere. Also, a new Focaler-Minimum Point Distance Intersection over Union (F-MPDIoU) loss function is proposed. It makes the model converge faster and deals with issues of leakage and false detection. Experiments were conducted on the expanded Vehicle Detection in Adverse Weather Nature dataset (DWAN). The results show significant improvements compared to the conventional You Only Look Once v7 (YOLOv7) model. The mean Average Precision (mAP@0.5), precision, and recall are enhanced by 4.2%, 8.3%, and 1.4%, respectively. The mean Average Precision is 86.5%. The frame rate is 68 frames per second (FPS), which meets the requirements for real-time detection. A generalization experiment was conducted using the autonomous driving dataset SODA10M. The mAP@0.5 achieved 56.7%, which is a 3.6% improvement over the original model. This result demonstrates the good generalization ability of the proposed method.

A Novel Non-Line-of-Sight Error Mitigation Algorithm Using Double Extended Kalman Filter for Ultra-Wide Band Ranging Technology

In complex indoor environments, target tracking performance is impacted by non-line-of-sight (NLOS) noises and other measurement errors. In order to fix NLOS errors, a Double Extended Kalman filter (DEKF) algorithm is proposed, which refers to a kind of cascaded structure composed of two Kalman filters. In the proposed algorithm, the first filter is a classic Kalman filter (KF) and the second is an Extended Kalman filter (EKF). The time of arrival (TOA) measurements collected by multiple stationary ultra-wide band (UWB) sensors are used. Residual errors between the measured TOA and the prediction from the first KF are used to adjust the covariance of the first KF accordingly. Then, we use the estimated distance state of the first KF as a measurement vector of the second EKF in order to obtain a smoother observation. One of the advantages of the proposed algorithm is that it is able to perform target tracking with a good accuracy even without or with only one line-of-sight(LOS) TOA measurement for a period of time without prior information of the NLOS noise, which may be difficult to obtain in practical applications. Another advantage is that the accuracy does not significantly decrease when NLOS noises persist for a long period of time. Finally, the proposed DEKF can maintain high-precision positioning characteristics in both the constant velocity (CV) model and the constant acceleration (CA) model for LOS/NLOS environments. In the case of mixed LOS/NLOS environments, the RMSE of the proposed algorithm can be kept within 5 cm, while the RMSEs of other compared algorithms are easily beyond tens of centimeters. At the same time, when the confidence of RMSE is set to 95% for 1000 MC simulations, the confidence interval of the proposed algorithm is the smallest, and the mean value is 3–5 times closer to the true value compared to other algorithms. Simulation and experimental results show that the proposed algorithm performs much better than other state-of-the-art algorithms, particularly in severe mixed LOS/NLOS environments.

Prediction of Land Image Classification using Squeeze Synchronization layer and Convolve Craft Focus Module in ResNet 101 Model

The classification of image is essential to make LULC (Land Use Land Cover) maps. However, the classification of land cover plays a vital role for studying and modernizing the land areas. Recently, deep learning (DL) techniques have achieved outstanding performance in the classification of high-resolution images. Different techniques have been employed in traditional methods to identify LULC due to its complex and ever-changing nature. However, these studies have shown improved outcomes despite some restrictions such as inaccuracies and reduced performance. To address these problems, the proposed study introduces a Squeeze Synchronization Layer (SSL) and a Convolve Craft Focus Module (CCFM) where, SSL reduces input data complexity by removing noise and irrelevant information from images using pooling and convolutional operations also, CCFM enhances feature extraction to improve land classification accuracy. The EUROSAT land image dataset is utilized for the evaluation of the introduced model. Whereas, the dataset comprises of 64x64 images, which are captured by satellite Sentinel-2A in ResNet 101 input layer. Although, a SSL is suggested, and a CCFM is implemented in the convolutional layer for classifying land images. However, the efficiency of the system is evaluated by measuring performance metrics such as recall, F1-score, precision, and accuracy values of the proposed system. The accuracy value of the proposed system is 96% of accuracy, 100% of precision, 100% of recall, and 100% of F1-score, signifies the superior efficiency of the proposed model

Exact reachability of discrete‐time stochastic system with multiplicative noise and partial information

AbstractThis paper is concerned with the exact reachability of discrete‐time stochastic system with partial information. The main contribution is to obtain the necessary and sufficient Gramian matrix criterion for the exact reachability of stochastic systems with partial information. The key is to innovatively transform the stochastic system governed by forward stochastic difference equation into an equivalent system governed by backward stochastic difference equation. Simulation examples illustrate the effectiveness of the results.

Multiscale feature enhanced gating network for atrial fibrillation detection.

Atrial fibrillation (AF) is a significant cause of life-threatening heart disease due to its potential to lead to stroke and heart failure. Although deep learning-assisted diagnosis of AF based on ECG holds significance in clinical settings, it remains unsatisfactory due to insufficient consideration of noise and redundant features. In this work, we propose a novel multiscale feature-enhanced gating network (MFEG Net) for AF diagnosis. The network integrates multiscale convolution, adaptive feature enhancement (FE), and dynamic temporal processing. The multiscale convolution helps capture global and local information. The FE module consists of a soft-threshold residual shrinkage component, a dilated convolution module, and a Squeeze-and-Excitation (SE) module, eliminating redundant features and emphasizing effective features. The design allows the network to focus on the most relevant AF features, thereby enhancing its robustness and accuracy in the presence of noise and irrelevant information. The dynamic temporal module helps the network learn and recognize the time dependence associated with AF. The novel design endows the model with excellent robustness to cope with random noise in real-world environments. Compared with the state-of-the-art methods, our model exhibits excellent classification performance with an accuracy of 0.930, an F1 score of 0.883, and remarkable resilience to noise interference on the PhysioNet Challenge 2017 dataset. Moreover, the model was trained on the CinC2017 database and validated on the CPSC2018 database and AFDB database, achieving accuracies of 0.908 and 0.938, respectively. The excellent classification performance of MFEG Net, coupled with its robustness in processing noisy electrocardiogram signals, makes it a powerful method for automatic atrial fibrillation detection. This method has made significant progress over state-of-the-art methods and may alleviate the burden of manual diagnosis for clinical doctors.

Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling.

Due to the labor-intensive manual annotations for nuclei segmentation, point-supervised segmentation based on nuclei coordinate supervision has gained recognition in recent years. Despite great progress, two challenges hinder the performance of weakly supervised nuclei segmentation methods: (1) The stable and effective segmentation of adjacent cell nuclei remains an unresolved challenge. (2) Existing approaches rely solely on initial pseudo-labels generated from point annotations for training, and inaccurate labels may lead the model to assimilate a considerable amount of noise information, thereby diminishing performance. To address these issues, we propose a method based on center-point prediction and pseudo-label updating for precise nuclei segmentation. First, we devise a Gaussian kernel mechanism that employs multi-scale Gaussian masks for multi-branch center-point prediction. The generated center points are utilized by the segmentation module to facilitate the effective separation of adjacent nuclei. Next, we introduce a point-guided attention mechanism that concentrates the segmentation module's attention around authentic point labels, reducing the noise impact caused by pseudo-labels. Finally, a label updating mechanism based on the exponential moving average (EMA) and k-means clustering is introduced to enhance the quality of pseudo-labels. The experimental results on three public datasets demonstrate that our approach has achieved state-of-the-art performance across multiple metrics. This method can significantly reduce annotation costs and reliance on clinical experts, facilitating large-scale dataset training and promoting the adoption of automated analysis in clinical applications.

Effect of the COVID-19 lockdown on background noise levels in Italian strong motion network

Italy has been majorly affected by the COVID-19 pandemic. The government declared a full lockdown and limited human and commercial activities to keep the pandemic under control. The limited human activity reduced the spread of the virus and the cultural noise it created. The effect of the lockdown is detected by the Italian strong motion network, which covers the entire country with their stations mostly located in the settlements. To assess the effect of the lockdown, background noise information up to 1 s from 2022 is used as a comparison. It is found that the background noise levels dropped around 1.46 dB during the lockdown, with a nationwide reduction in almost all of the stations. Noise levels have dropped both in the daytime and nighttime during the lockdown, with a more significant noise drop during the nighttime, which can be linked to the ban on dining in restaurants and bars and the curfew. A similar trend is found in weekday and weekend comparisons; in both time ranges, 2022 was noisier regarding the lockdown period. Stations located in public spaces such as schools and city halls observed noise reduction of up to 7.99 dB, and this noise level reduction is visible in major cities. We analyzed the 10 most populated Italian cities and their surroundings and found noise reduction of up to 5.5 and 2.1 dB in the median.

A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion.

Convolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge feature extraction and saturated performance problems. To solve these problems, this paper proposes a two-branch convolutional image denoising network based on nonparametric attention and multiscale feature fusion, aiming to improve the denoising performance while better recovering the image edge and texture information. Firstly, ordinary convolutional layers were used to extract shallow features of noise in the image. Then, a combination of two-branch networks with different and complementary structures was used to extract deep features from the noise information in the image to solve the problem of insufficient feature extraction by the single-branch network model. The upper branch network used densely connected blocks to extract local features of the noise in the image. The lower branch network used multiple dilation convolution residual blocks with different dilation rates to increase the receptive field and extend more contextual information to obtain the global features of the noise in the image. It not only solved the problem of insufficient edge feature extraction but also solved the problem of the saturation of deep CNN performance. In this paper, a nonparametric attention mechanism is introduced in the two-branch feature extraction module, which enabled the network to pay attention to and learn the key information in the feature map, and improved the learning performance of the network. The enhanced features were then processed through the multiscale feature fusion module to obtain multiscale image feature information at different depths to obtain more robust fused features. Finally, the shallow features and deep features were summed using a long jump join and were processed through an ordinary convolutional layer and output to obtain a residual image. In this paper, Set12, BSD68, Set5, CBSD68, and SIDD are used as a test dataset to which different intensities of Gaussian white noise were added for testing and compared with several mainstream denoising methods currently available. The experimental results showed that this paper's algorithm had better objective indexes on all test sets and outperformed the comparison algorithms. The method in this paper not only achieved a good denoising effect but also effectively retained the edge and texture information of the original image. The proposed method provided a new idea for the study of deep neural networks in the field of image denoising.

Integration of gene expression and DNA methylation data using MLA-GNN for liver cancer biomarker mining

The early symptoms of hepatocellular carcinoma patients are often subtle and easily overlooked. By the time patients exhibit noticeable symptoms, the disease has typically progressed to middle or late stages, missing optimal treatment opportunities. Therefore, discovering biomarkers is essential for elucidating their functions for the early diagnosis and prevention. In practical research, challenges such as high-dimensional features, low sample size, and the complexity of gene interactions impact the reliability of biomarker discovery and disease diagnosis when using single-omics approaches. To address these challenges, we thus propose, Multi-level attention graph neural network (MLA-GNN) model for analyzing integrated multi-omics data related to liver cancer. The proposed protocol are using feature selection strategy by removing the noise and redundant information from gene expression and DNA methylation data. Additionally, it employs the Cartesian product method to integrate multi-omics datasets. The study also analyzes gene interactions using WGCNA and identifies potential genes through the MLA-GNN model, offering innovative approaches to resolve these issues. Furthermore, this paper identifies FOXL2 as a promising liver cancer marker through gene ontology and survival analysis. Validation using box plots showed that the expression of the gene FOXL2 was higher in patients with hepatocellular carcinoma than in normal individuals. The drug sensitivity correlation and molecular docking results of FOXL2 with the liver cancer-targeting agent lenvatinib emphasized its potential role in hepatocellular carcinoma treatment and highlighted the importance of FOXL2 in hepatocellular carcinoma treatment.

Lightweight global adaptive feature enhancement network for underwater object detection with sonar image

Abstract Sonar target detection is widely used in various underwater detection tasks. However, sonar images often lack target information and blurry features because of the interference of seabed environmental noise and complex background information. This poses significant challenges for sonar target detection tasks. A new lightweight Global Adaptive Feature Enhancement Network (GAFE-Net) is proposed to enhance the ability to acquire target information and effectively suppress background information. This network utilizes lightweight convolutional calculations and adaptive feature extraction blocks with global feature extraction capabilities to capture multi-scale semantic features of sonar images. Specifically, GhostConv is used to maintain local feature extraction capability while reducing computation complexity. The adaptive feature extraction block (C2FC) is employed to capture deep semantic features. The partial self-attention (PSA) mechanism is adopted to enhance the capability of focusing on targets. The Slim-neck is deployed for fusing multi-scale information. Validation on the public sonar image dataset URPC2021 shows that, compared to other advanced sonar target detection algorithms, the proposed method improves accuracy while maintaining low computational complexity, demonstrating excellent performance.

Robust Principal Component Analysis Based on Fuzzy Local Information Reservation.

Principal Component Analysis (PCA) aims to acquire the principal component space containing the essential structure of data, instead of being used for mining and extracting the essential structure of data. In other words, the principal component space contains not only information related to the essential structure of data but also some unrelated information. This frequently occurs when the intrinsic dimensionality of data is unknown or when it has complex distribution characteristics such as multi-modalities, manifolds, etc. Therefore, it is unreasonable to identify noise and useful information based solely on reconstruction error. For this reason, PCA is unsuitable as a preprocessing technique for most applications, especially in noisy environment. To solve this problem, this paper proposes robust PCA based on fuzzy local information reservation (FLIPCA). By analyzing the impact of reconstruction error on sample discriminability, FLIPCA provides a theoretical basis for noise identification and processing. This not only greatly improves its robustness but also extends its applicability and effectiveness as a data preprocessing technique. Meanwhile, FLIPCA maintains consistent mathematical descriptions with traditional PCA while having few adjustable hyperparameters and low algorithmic complexity. Finally, we conducted comprehensive experiments on synthetic and real-world datasets, which substantiated the superiority of our proposed algorithm.

A framework of multi-view machine learning for biological spectral unmixing of fluorophores with overlapping excitation and emission spectra.

The accuracy of assigning fluorophore identity and abundance, known as spectral unmixing, in biological fluorescence microscopy images remains a significant challenge due to the substantial overlap in emission spectra among fluorophores. In traditional laser scanning confocal spectral microscopy, fluorophore information is acquired by recording emission spectra with a single combination of discrete excitation wavelengths. However, organic fluorophores possess characteristic excitation spectra in addition to their unique emission spectral signatures. In this paper, we propose a generalized multi-view machine learning approach that leverages both excitation and emission spectra to significantly improve the accuracy in differentiating multiple highly overlapping fluorophores in a single image. By recording emission spectra of the same field with multiple combinations of excitation wavelengths, we obtain data representing different views of the underlying fluorophore distribution in the sample. We then propose a multi-view machine learning framework that allows for the flexible incorporation of noise information and abundance constraints, enabling the extraction of spectral signatures from reference images and efficient recovery of corresponding abundances in unknown mixed images. Numerical experiments on simulated image data demonstrate the method's efficacy in improving accuracy, allowing for the discrimination of 100 fluorophores with highly overlapping spectra. Furthermore, validation on images of mixtures of fluorescently labeled Escherichia coli highlights the power of the proposed multi-view strategy in discriminating fluorophores with spectral overlap in real biological images.

Semantic-Driven Crossmodal Fusion for Multimodal Sentiment Analysis

In multimodal sentiment analysis (MSA), the fusion strategies of multimodal features significantly influence the performance of MSA models. Previous works frequently face challenges in integrating heterogeneous data without fully leveraging the rich semantic content of text, resulting in poor information association. We propose an MSA model based on Text-Driven Crossmodal Fusion and Mutual Information Estimation, called TeD-MI, which comprises a Stacked Text-Driven Crossmodal Fusion (STDC) module, which efficiently fusions the three modalities driven by the text modality to optimize fusion feature representation and enhance semantic understanding. Furthermore, TeD-MI designed a mutual information estimation module to achieve the best balance between preserving task-related information and filtering out irrelevant noise information as much as possible. Comprehensive experiments conducted on the CMU-MOSI and CMU-MOSEI datasets demonstrate our proposed model achieves varying degrees of improvement across most evaluation metrics.

Fault diagnosis of the harmonic reducer based on dual-path convolutional network with multi-channel hybrid attention mechanism

Abstract Harmonic reducers are prone to wear and other failures due to continuous deformation of the flexure wheel during operation. To evaluate the sorts of faults in harmonic reducers under various operating situations, a fault diagnosis method (MADCNN-BiGRU) based on dual-path convolution neural network (DCNN) with multi-channel hybrid attention mechanism (MCHAM) and bidirectional gated recurrent unit (BiGRU) was proposed. Firstly, a novel MCHAM focuses on the critical information part of the vibration signal and can effectively suppress the noise and redundant information. Secondly, a new soft threshold function is constructed to regulate the attention weights dynamically. Thirdly, BiGRU obtains feature information for different time series locations to identify the faults correctly. Fourthly, a harmonic reducer test rig is established to collect vibration signals of harmonic reducers with different faults under various operating situations. The comparative experimental results demonstrate that MADCNN-BiGRU has excellent capacity for robustness and generalization, and can effectively diagnose under various operating situations.

JOINT LOW-RANK REPRESENTATION AND TRANSFER MULTI-VIEW LEARNING APPROACH FOR EEG EPILEPSY CLASSIFICATION

Classification of epileptic electroencephalograms can provide strong technical support for early warning and monitoring of epilepsy. However, Electroencephalogram (EEG) has characteristics of specificity, multi-feature, and susceptibility to noise interference. Therefore, this paper proposes a joint low-rank representation and transfer multi-view learning (LRR-TML) approach for EEG epilepsy classification. The LRR-TML approach inherits the advantages of low-rank representation, filters redundant features and noise information, and focuses on the main feature and significant feature of EEG. The LRR-TML approach introduces the ideas of transfer learning and multi-view learning on the framework of low-rank representation, mines the linear correlation between different domains in the subspace through low-rank representation, and effectively transfers the discriminative knowledge of the source domain to the target domain. According to the principles of complementarity and consistency of views, the LRR-TML approach comprehensively describes EEG by integrating various features, maximizing the consistency between different views and minimizing the divergence between views. Experimental results on the Bonn dataset show that the LRR-TML approach achieves the best classification results among all comparison approaches.

Research on the Quantitative Inversion of Soil Iron Oxide Content Using Hyperspectral Remote Sensing and Machine Learning Algorithms in the Lufeng Annular Structural Area of Yunnan, China.

This study used hyperspectral remote sensing to rapidly, economically, and non-destructively determine the soil iron oxide content of the Dinosaur Valley annular tectonic region of Lufeng, Yunnan Province. The laboratory determined the iron oxide content and original spectral reflectance (OR) in 138 surface soil samples. We first subjected the OR data to Savizky-Golay smoothing, followed by four spectral transformations-continuum removal reflectance, reciprocal logarithm reflectance, standard normal variate reflectance, and first-order differential reflectance-which improved the signal-to-noise ratio of the spectral curves and highlighted the spectral features. Then, we combined the correlation coefficient method (CC), competitive adaptive reweighting algorithm, and Boruta algorithm to screen out the characteristic wavelength. From this, we constructed the linear partial least squares regression model, nonlinear random forest, and XGBoost machine learning algorithms. The results show that the CC-Boruta method can effectively remove any noise and irrelevant information to improve the model's accuracy and stability. The XGBoost nonlinear machine learning algorithm model better captures the complex nonlinear relationship between the spectra and iron oxide content, thus improving its accuracy. This provides a relevant reference for the rapid and accurate inversion of iron oxide content in soil using hyperspectral data.

Non-Destructive Analysis for Machine-Picked Tea Leaf Composition Using Near-Infrared Spectroscopy Combined Chemometric Methods

This paper aimed to predict the mechanical composition of machine-picked fresh tea leaves (MPFTLs) using near-infrared spectroscopy (NIRS) rapidly and non-destructively. Samples of MPFTL with different mechanical composition ratios were collected and subjected to NIRS analysis. Subsequently, various preprocessing methods were employed to eliminate extraneous noise information. Next, characteristic spectral information was extracted using the backward interval partial least squares (biPLS) method, which was subsequently subjected to principal component analysis (PCA). Finally, a predictive model was constructed by applying the back propagation artificial neural network (BP-ANN) method, which was tested by external samples to assess its predictive efficacy, and the results were expressed as root mean square error and determination coefficient of prediction (Rp2). The optimal spectral pretreatment method was the following: (standard normal variate (SNV) + second derivative (SD)). Four characteristic spectral subintervals of ([2, 3, 7, 10]) were screened out, and the cumulative contribution rate of 95.20%, attributable to the first three principal components, was determined. When the tanh transfer function was applied to construct the BP-ANN-NIRS model, the results demonstrated optimal performance, exhibiting a root mean square error and a determination coefficient of prediction (Rp2) of 0.976 and 0.027, respectively. The absolute values of prediction deviation for all prediction set samples were found to be less than 0.04. The results of the best BP-ANN model for external samples were found to be in close agreement with those of the prediction set model. NIRS technology has successfully achieved the forecasting of the mechanical composition of machine-picked fresh tea leaves rapidly and accurately, providing a fair and convenient new method for purchasing fresh tea raw materials by machines, according to their quality, and promoting the sustainable high-quality and healthy development of the tea industry.

Bluetooth Active Noise Cancellation (ANC) Technology: Principles, Integration, Applications, and Performance Evaluation

Thanks to the development of wireless audio transmission technology, Bluetooth technology made it possible to give users a seamless and convenient connection experience. However, in a noisy environment, preserving the audio quality is still difficult, which motivated people to explore the technology of Active Noise Cancellation (ANC). This paper aims to analyze integration of ANC technology and the potential of Bluetooth audio transmitting to improve the audio effect, especially in application scenarios where clear audio is required. At the beginning of this paper, how Bluetooth and ANC are evolved is reviewed for the preparatory works. Then, the technology mechanism of the ANC and the advantages in particular application scenarios such as Industrial automation and autonomous vehicles are described in detail. In addition, this paper give experimental data to illustrate that the ANC systems is effective in reducing ambient noise. The central technology of ANC system is using a microphone to detect environmental noise information and then creates a sound wave phase-inverted to the original noise to cancel. Furthermore, by connecting artificial intelligence technique, the ANC system dynamically fits from different acoustic environment to improve the audio quality. Finally, the experimental results that are quoted reveal that the advanced ANC system can reduce the noise level in a wide range of environments, cutting down the sound pressure level to 65 dB. It is clearly demonstrated that the ANC technology is widely applicable and effective to enhance the audio experience.

Improved algorithm for fracture-dissolution pore detection in resistivity imaging logging based on dung beetle optimization

Abstract Resistivity imaging logging has become a direct and precise method for visualizing the structural complexities of reservoir fractures and dissolution pores. The current use of Otsu's thresholding for segmentation results in poor segmentation quality and significant noise. Accurate segmentation of sub-images containing fracture and dissolution pore targets is essential for automated structure identification and subsequent parameter calculation. This study leverages the rapid convergence and robust global optimization capabilities of the Dung Beetle Optimizer to develop enhanced image segmentation approaches. Specifically, it introduces a refined K-Means algorithm for multi-category image segmentation and an Otsu algorithm for multi-threshold image segmentation, both optimized by the Dung Beetle Optimizer. Compared to conventional binary segmentation algorithms, this new algorithm effectively isolates noise and extracts multi-category information. Using the segmented sub-images, this paper integrates mathematical morphology techniques to compute parameters such as area, perimeter, tortuosity length, and pore shape factor for identified targets. Additionally, Principal Component Analysis is used to derive recognition factors for fractures and dissolution pores. Applications show that this factor can identify matrix, fracture, and dissolution pore targets in complex background images. By combining parameter information of the target area, the method effectively removes false information in resistivity imaging and segments sub-images of fractures and dissolution pores, calculating fracture area ratio, dissolution pore area ratio, and total area ratio.