Sobel Operator Research Articles

Non-uniformly lighted image enhancement exploiting the Atangana–Baleanu fractional integral and the Sobel filter

Improving the quality of non-uniformly lighted images is particularly hard because these images contain regions with different brightness, thus needing a different level of enhancement. Recently, this problem has been addressed by combining the input image with a new version where the input brightness is linearly up-scaled by a parameter, which is estimated without supervision by segmenting the image into dark and bright regions and comparing their brightness. This estimate represents a pitfall because it makes the algorithm performance dependent on the segmentation accuracy and cannot be applied to images whose bright regions are almost white. To overcome these issues, the present work introduces a new image-aware estimate of the brightness up-scaling parameter, which exploits edge information extracted by the Sobel filter and by the first derivative of the Atangana–Baleanu fractional integral. The joint use of integer- and fractional-order calculus proposed here represents the main contribution of this work and, as proved by the experiments, enables us to reach a good level of enhancement surpassing other cutting-edge techniques, particularly in terms of reduced artifact production.

An Infrared and Visible Image Alignment Method Based on Gradient Distribution Properties and Scale-Invariant Features in Electric Power Scenes.

In grid intelligent inspection systems, automatic registration of infrared and visible light images in power scenes is a crucial research technology. Since there are obvious differences in key attributes between visible and infrared images, direct alignment is often difficult to achieve the expected results. To overcome the high difficulty of aligning infrared and visible light images, an image alignment method is proposed in this paper. First, we use the Sobel operator to extract the edge information of the image pair. Second, the feature points in the edges are recognised by a curvature scale space (CSS) corner detector. Third, the Histogram of Orientation Gradients (HOG) is extracted as the gradient distribution characteristics of the feature points, which are normalised with the Scale Invariant Feature Transform (SIFT) algorithm to form feature descriptors. Finally, initial matching and accurate matching are achieved by the improved fast approximate nearest-neighbour matching method and adaptive thresholding, respectively. Experiments show that this method can robustly match the feature points of image pairs under rotation, scale, and viewpoint differences, and achieves excellent matching results.

Identification of Lighting Strike Damage and Prediction of Residual Strength of Carbon Fiber-Reinforced Polymer Laminates Using a Machine Learning Approach.

Due to the complex and uncertain physics of lightning strike on carbon fiber-reinforced polymer (CFRP) laminates, conventional numerical simulation methods for assessing the residual strength of lightning-damaged CFRP laminates are highly time-consuming and far from pretty. To overcome these challenges, this study proposes a new prediction method for the residual strength of CFRP laminates based on machine learning. A diverse dataset is acquired and augmented from photographs of lightning strike damage areas, C-scan images, mechanical performance data, layup details, and lightning current parameters. Original lightning strike images, preprocessed with the Sobel operator for edge enhancement, are fed into a UNet neural network using four channels to detect damaged areas. These identified areas, along with lightning parameters and layup details, are inputs for a neural network predicting the damage depth in CFRP laminates. Due to its close relation to residual strength, damage depth is then used to estimate the residual strength of lightning-damaged CFRP laminates. The effectiveness of the current method is confirmed, with the mean Intersection over Union (mIoU) achieving over 93% for damage identification, the Mean Absolute Error (MAE) reducing to 5.4% for damage depth prediction, and the Mean Relative Error (MRE) reducing to 7.6% for residual strength prediction, respectively.

Automated CAD system for early detection and classification of pancreatic cancer using deep learning model

Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally. Manual identification of pancreatic tumors by radiologists is challenging and time-consuming due to the complex nature of CT scan images and variations in tumor shape, size, and location of the pancreatic tumor also make it challenging to detect and classify different types of tumors. Thus, to address this challenge we proposed a four-stage framework of computer-aided diagnosis systems. In the preprocessing stage, the input image resizes into 227 × 227 dimensions then converts the RGB image into a grayscale image, and enhances the image by removing noise without blurring edges by applying anisotropic diffusion filtering. In the segmentation stage, the preprocessed grayscale image a binary image is created based on a threshold, highlighting the edges by Sobel filtering, and watershed segmentation to segment the tumor region and we also implement the U-Net method for segmentation. Then refine the geometric structure of the image using morphological operation and extracting the texture features from the image using a gray-level co-occurrence matrix computed by analyzing the spatial relationship of pixel intensities in the refined image, counting the occurrences of pixel pairs with specific intensity values and spatial relationships. The detection stage analyzes the tumor region’s extracted features characteristics by labeling the connected components and selecting the region with the highest density to locate the tumor area, achieving a good accuracy of 99.64%. In the classification stage, the system classifies the detected tumor into the normal, pancreatic tumor, then into benign, pre-malignant, or malignant using a proposed reduced 11-layer AlexNet model. The classification stage attained an accuracy level of 98.72%, an AUC of 0.9979, and an overall system average processing time of 1.51 seconds, demonstrating the capability of the system to effectively and efficiently identify and classify pancreatic cancers.

Method for Preprocessing Video Data for Training Deep-Learning Models for Identifying Behavioral Events in Bio-Objects

Monitoring moving bio-objects is currently of great interest for both fundamental and practical research. The advent of deep-learning algorithms has made it possible to automate the qualitative and quantitative analysis of the behavior of bio-objects recorded in video format. When processing such data, it is necessary to consider additional factors, such as background noise in the frame, the speed of the bio-object, and the need to reflect information about the previous (past) and subsequent (future) pose of the bio-object in one video frame. The preprocessed dataset must be suitable for verification by experts. This article proposes a method for preprocessing data to identify the behavior of a bio-object, a clear example of which is experiments on laboratory animals with the collection of video data. The method is based on combining information about a behavioral event presented in a sequence of frames with the addition of a native image and subsequent boundary detection using the Sobel filter. The resulting representation of a behavioral event is easily perceived by both human experts and neural networks of various architectures. The article presents the results of training several neural networks on the obtained dataset and proposes an effective neural network architecture (F1-score = 0.95) for identifying discrete events of biological objects’ behavior.

Object Detection Based on Edge Enhanced YOLOv5

Abstract In order to address issues that arise in complex scenarios, an edge-enhanced YOLOv5 algorithm is proposed. The fusion of edge features with original feature maps serves to enhance the accuracy of the detection process. The algorithm employs Sobel and Canny operators for edge extraction, integrating these with low-level feature maps through addition and concatenation. Testing in urban and desert settings demonstrated that the enhanced models exhibited superior accuracy and a reduction in false and missed detection compared to the original YOLOv5. This methodology has the potential to enhance target detection performance in complex environments.

Enhanced subsurface edge detection using a 3D integration operator

Orientation analysis is widely employed in edge and line detection and is an important step in object detection and illumination. The direct application of a 3D integration orientation operator to a seismic image is proposed to enhance efficiency and improve delineation of subsurface edges. The primary objective in doing so is to overcome the weaknesses associated with gradient operator-based edge detections, namely, noise sensitivity due to using a fixed smoothing window and orientation bias of the gradient operators. Our study shows that the application of a 3D integration operator to a seismic data volume results in more coherent edges than the gradient-based Sobel operator. In addition, the computation efficiency improves significantly in comparison to a direct 2D integration operator.

SEGMENTASI LESI KULIT MONKEYPOX MENGGUNAKAN ARSITEKTUR U-NET

Monkeypox has become an issue of global concern as cases continue to rise across various countries. While most cases display mild symptoms, severe infections can lead to life-threatening complications. Early detection is therefore crucial to control the spread of monkeypox, with one approach being the segmentation of monkeypox lesions to differentiate affected areas from healthy skin and remove any interfering elements, such as hair, which can enhance model accuracy. In this consider, due to the inaccessibility of labeled masks, mask information were produced utilizing watershed segmentation with the sobel operator and otsu thresholding. The division of skin injuries was in this way carried out with a U-Net demonstrate, utilizing MobileNetV2 as the backbone and ImageNet weights for transfer learning. The U-Net model reached an accuracy of 88.07%, though some signs of overfitting were observed, likely due to low-quality label information from the watershed labeling process, which necessitates parameter tuning.

Weakly-Supervised Hair SEM Microscope Image Segmentation Using a Priori Structure Information

The analysis of microscopic images of hair has a wide range of applications in the domains of cosmetics, healthcare and forensics. The segmentation of the hair represents the initial step of automatic large-scale quantitative analysis of microscopic images of hair. It ensures that subsequent quantitative measurements are performed in an appropriate area corresponding to the hair, avoiding artifacts near the boundaries. This process can be time-consuming, tedious and susceptible to subjective errors when conducted by a human operator. Deep learning methods represent a promising solution; however, obtaining pixel-level accurate masks is a costly process. This paper presents a novel weakly supervised pipeline for the segmentation of hair SEM (Scanning Electron Microscope) microscopic images, which requires only simple image-level annotations for training. The proposed method incorporates the Radon transform, the Sobel operator and a novel Boundary Discrimination Module (BD-module) for the estimation of the presence of boundaries. The proposed pipeline was evaluated on a recently collected hair SEM dataset (429 images). Furthermore, it is benchmarked with methods including Unet and Segment Anything Model (SAM). The results demonstrated a mean Hausdorff Distance improvement of over 30% and a standard deviation improvement of over 50% in comparison with the Unet and SAM. Moreover, we proposed additional refinement modules to address boundary nonlinear cases and conducted Grad-CAM analysis to enhance the interpretability of the BD-module. Additionally, we proposed a novel quality estimation metric based on gradient map for self-quality assessment. The SEM hair dataset is accessible to the research community in an open-source format.

Lightweight UAV Small Target Detection and Perception Based on Improved YOLOv8-E

Traditional unmanned aerial vehicle (UAV) detection methods struggle with multi-scale variations during flight, complex backgrounds, and low accuracy, whereas existing deep learning detection methods have high accuracy but high dependence on equipment, making it difficult to detect small UAV targets efficiently. To address the above challenges, this paper proposes an improved lightweight high-precision model, YOLOv8-E (Enhanced YOLOv8), for the fast and accurate detection and identification of small UAVs in complex environments. First, a Sobel filter is introduced to enhance the C2f module to form the C2f-ESCFFM (Edge-Sensitive Cross-Stage Feature Fusion Module) module, which achieves higher computational efficiency and feature representation capacity while preserving detection accuracy as much as possible by fusing the SobelConv branch for edge extraction and the convolution branch to extract spatial information. Second, the neck network is based on the HSFPN (High-level Screening-feature Pyramid Network) architecture, and the CAA (Context Anchor Attention) mechanism is introduced to enhance the semantic parsing of low-level features to form a new CAHS-FPN (Context-Augmented Hierarchical Scale Feature Pyramid Network) network, enabling the fusion of deep and shallow features. This improves the feature representation capability of the model, allowing it to detect targets of different sizes efficiently. Finally, the optimized detail-enhanced convolution (DEConv) technique is introduced into the head network, forming the LSCOD (Lightweight Shared Convolutional Object Detector Head) module, enhancing the generalization ability of the model by integrating a priori information and adopting the strategy of shared convolution. This ensures that the model enhances its localization and classification performance without increasing parameters or computational costs, thus effectively improving the detection performance of small UAV targets. The experimental results show that compared with the baseline model, the YOLOv8-E model achieved (mean average precision at IoU = 0.5) an mAP@0.5 improvement of 6.3%, reaching 98.4%, whereas the model parameter scale was reduced by more than 50%. Overall, YOLOv8-E significantly reduces the demand for computational resources while ensuring high-precision detection.

A steel defect detection method based on edge feature extraction via the Sobel operator

Scratches and cracks in steel severely affect its service life and performance. However, owing to the irregular shapes and sizes of steel surface defects, defects within the same class may be different, whereas defects between classes may be similar. Existing methods focus only on spatial information, resulting in low detection accuracy. To alleviate these problems, this paper proposes the ECDY (EIFEM CARAFE DyHead) network to enhance the detection capability of steel defects. We first design a feature extraction module that focuses on the edge information of feature contours. This module uses the Sobel operator to extract the edge information of a feature and fuses it with the overall spatial information so that richer semantic information can be obtained. The module has improved accuracy in the YOLOv5, YOLOv8, and YOLOv10 versions, and uses fewer parameters and calculations. In particular, in YOLOv8x, mAP@0.5 increased by 2.5%, and the number of parameters was reduced by 12.4 M. Second, to retain the detailed information in the feature pyramid, and to better reconstruct features, we choose the content-aware reassembly feature method (CARAFE) as the upsampling method. Finally, the detection head was replaced with a dynamic unified detection head (DyHead) to adapt to different defect sizes and different task requirements. Compared with YOLOv8s, the proposed method improves precision by 1.6%, recall by 4%, and mAP@0.5 by 4%. This value is 4.2% higher than the mAP@0.5 of the current SOTA model RT-DETR-L in the field of object detection and has 23.2 M fewer parameters.

Combination of edge enhancement and cold diffusion model for low dose CT image denoising.

Since the quality of low dose CT (LDCT) images is often severely affected by noise and artifacts, it is very important to maintain high quality CT images while effectively reducing the radiation dose. In recent years, the representation of diffusion models to produce high quality images and stable trainability has attracted wide attention. With the extension of the cold diffusion model to the classical diffusion model, its application has greater flexibility. Inspired by the cold diffusion model, we proposes a low dose CT image denoising method, called CECDM, based on the combination of edge enhancement and cold diffusion model. The LDCT image is taken as the end point (forward) of the diffusion process and the starting point (reverse) of the sampling process. Improved sobel operator and Convolution Block Attention Module are added to the network, and compound loss function is adopted. The experimental results show that CECDM can effectively remove noise and artifacts from LDCT images while the inference time of a single image is reduced to 0.41 s. Compared with the existing LDCT image post-processing methods, CECDM has a significant improvement in all indexes.

MAxPy: A Framework for Bridging Approximate Computing Circuits to Its Applications

This paper presents MAxPy, a framework for bridging approximate computing (AxC) circuit design to its applications. MAxPy is an application-agnostic framework able to automatically build a cycle-accurate Python model of an approximate hardware design. This model can easily be emulated and integrated as a module in Python-based applications. We are herein proposing this framework aiming to build an AxC toolbox stimulating open research in academia to promote the integration of the existing and future open-source (i) AxC benchmarks, (ii) approximate logic synthesis tools, and (iii) P unit insights. In this paper, we demonstrate the use of MAxPy to explore the AxC design space of a Sobel filter hardware design as a case study exploiting a set of approximate adders combined with the data-driven approximate logic synthesis via probabilistic pruning. Then, we present a Pareto front for circuit area, energy, and delay reduction versus the application-level metrics: edge detection accuracy and structural similarity index (SSIM). The Pareto front results of the multiple AxC techniques herein explored show circuit area savings ranging from 40.4% to 59.1% for a 98.1% to 99.6% accuracy on edge detection application MAxPy code is open source in: github.com/MAxPy-Project

Research on Grading Method for Jin Guan Apples based on Machine Vision

As the demand for apples increases, so do the expectations for apple quality. To address the time-consuming and labor-intensive nature of apple grading, this paper focuses on the Jin-Guan apple variety and applies machine vision techniques to extract three key features: maximum transverse diameter, shape index, and surface color. A backpropagation neural network (BP neural network) is employed to achieve accurate grading of the apples. First, by analyzing the relationships between the RGB color space components in the original images, the apple images are extracted using the method of comparing the R channel value to the B channel value. Gaussian filtering is then applied to remove image noise. Second, the complete apple contour is extracted using an improved Sobel operator method. The maximum transverse diameter and shape index of the apple are calculated based on the pixel coordinates along the contour, and the apple's surface color is determined using a nearest-neighbor classifier. Finally, a BP neural network model is established and trained to perform the grading. A total of 160 apples were used for training, and 140 for testing. The results show that the grading accuracy reached 92.14%, and the grading efficiency was approximately 1.7 times that of a skilled human grader. This grading method provides a technical reference for apple grading processes.

Adaptive Threshold Algorithm for Outlier Elimination in 3D Topography Data of Metal Additive Manufactured Surfaces Obtained from Focus Variation Microscopy

The topography of surfaces produced by metal additive manufacturing is a challenge for optical measurement systems such as focus variation microscopes. These irregularities can lead to artifacts, such as incorrectly measured protrusions or spikes, hampering reliable topographic characterization. In order to eliminate this problem, we introduce a new algorithm based on dual convolving a vertical Sobel operator with cross sections of an image stack parallel to the scanning direction of the so-called depth scan. This has proven beneficial in order to distinguish the focus region from out-of-focus areas where outliers are frequently detected. This paper introduces a method for deriving self-adaptive thresholds from the convolution result and compares the effects of different operators in creating self-adaptive thresholds. Additionally, a simulation model of focus variation microscopy is introduced to validate both the measuring system and the proposed algorithm, thereby enhancing the overall performance of focus variation microscopy. Finally, comparisons of measurement results on rough metal additive manufacturing workpieces with and without self-adaptive thresholds are discussed to demonstrate the algorithm’s effectiveness.The utilization of self-adaptive thresholds demonstrably reduces the uncertainty range in roughness parameter calculations. For example, in the case of an additive manufactured metal sample due to outlier elimination, the Sz roughness value reduces from 543 µm to 413 µm.

Improvement of Criminisi’s Stripe Noise Suppression Method for Side-Scan Sonar Images

In response to the problem of stripe noise significantly reducing the clarity and details of side-scan sonar images due to various factors, the authors of this paper propose an improved Criminisi method for stripe noise suppression. To address the issues encountered in the Criminisi algorithm during the suppression of stripe noise in side-scan sonar images, the following steps are suggested: firstly, introduce dynamic weights in the priority calculation to adaptively adjust the confidence and data term weights based on the current patch’s texture complexity; secondly, utilize the Sobel operator in the data term calculation to capture the image edge information more accurately; and, thirdly, optimize the matching block search process by introducing the Manhattan distance in addition to the Sum of Squared Differences (SSD) criterion to further select the best matching block while transitioning from a global search to a local search. Experimental validation was conducted using simulated stripe noise images, comparing the proposed method with four traditional denoising techniques. The results demonstrate that the denoising effectiveness of the proposed method is superior, effectively restoring texture in noisy regions while preserving texture structure integrity. Ablation experiments validate the effectiveness of the proposed improvements. Denoising experiments on real noisy images show satisfactory results with this method, and combining it with Fourier transform for additional smoothing in certain cases may yield even better results.

Visual Communication Method of Multi-Frame Film and Television Special Effects Images Based on Deep Learning

In the view of art form change, there is a perspective problem in the detail part of multi-frame film and television special effects (TSEs) images in the special effects rendering module, which leads to the greater ambiguity of multi-frame film and TSEs images. In order to advance the talent of identifying the detail structures and characteristics of multi-frame film and TSEs images, a visual communication technology of multi-frame film and TSEs images in the view of art form change grounded on deep learning is suggested. The transmission relation model of detail characteristics of multi-frame film and TSEs images in the field of artistic form change is constructed, and the attenuation treatment of detail action special effects of multi-frame film and TSEs images in the field of artistic form change is carried out with the assistance of deep learning approaches. The detailed information and color of multi-frame film and TSEs images are obtained by using prior map knowledge instead of rendering basic color. Over the technique of filtering and preserving, the multi-frame film and TSEs images are directed to screen the detailed feature quantity of the input film and TSEs images in the field of artistic form change. In addition, the Sobel operator is practiced to perceive the scrawny edge information of multi-frame film and TSEs images, and block fitting interpolation and depth filtering analysis are used in the edge area to realize the visual communication and color feature parameter identification and amplification of multi-frame film and TSEs images in the field of artistic form change, so as to improve the fuzzy enhancement of multi-frame film and TSEs images. The test results and results indicate that the average Peak signal-to-noise ratio (PSNR) of the proposed method is about 37.7166[Formula: see text]dB, which is 49.08% and 52.08% higher than the PSNRs of the Harris method and SUFR method, respectively. It has been confirmed that this scheme can successfully solve the edge blurring and distortion problems of multi-frame movies and image images in the field of art form change.

Research on coal-rock boundary identification based on the morphological sobel algorithm

Due to the harsh underground environment during coal mining, the quality of images collected by cameras is not sufficient, and the acquired images are greatly affected by noise, affecting visual observation; to a certain extent, subsequent intelligent mining is limited. A morphological Sobel coal-rock boundary recognition algorithm is proposed according to the different gray levels of coal-rock images to solve the problem of coal image quality. First, the details of the coal and rock images are smoothly preprocessed to improve the contrast between the feature boundaries and surrounding pixels, and the gray-level adaptive threshold is applied after processing. Morphological corrosion theory is used to process the morphological structure in an image, and the corresponding boundary in the image is extracted for recognition. Compared with the boundary points identified by each algorithm, the area error of coal and rock identification is calculated by using the boundary point fitting curve. The morphological Sobel algorithm is used to calculate the identification area error of coal and rock at different angles according to the camera range. The experimental results show that the boundaries identified by the morphological Sobel algorithm have the best degree of overlap with the boundaries of the original image. The identification error area is only about 10% of the Sobel operator and Canny operator algorithm. Monitoring coal and rock specimens can enable the effective identification of coal and rock boundaries from various angles.

Algorithms and software for processing images of the structure of metallic materials for the purpose of determining creep characteristics

The paper contains a description of the approach, algorithms, and software tool for image analysis of deformed material structures. The developed approach is based on the analysis of the microstructure of the material to identify important factors that affect high-temperature deformation during creep in a heat-resistant nickel-based alloy, namely, the dimensions of the channels and their orientation. The developed software tool uses algorithms for converting images into binary black and white using the Otsu method. The intensity of the gradient at each point of the image is visualized using the Sobel operator. Fragment boundaries are determined using the Canny edge detector. Straight line segments are found using the Hough transformation. The software tool is implemented in the Python programming language using the OpenCV library. The main components are described and a flow chart of the program is provided. With the use of the developed software, the transformation of the known experimentally obtained images of the structures of the specimens from heat-resistant nickel-based alloy CMSX-4 deformed at a temperature of 1273K and in a wide range of stresses at different time moments was performed. The results of the analysis of the dimensions of the g-phase channels in the alloy using quantitative evaluation of transformed binary images are discussed. The found characteristics were matched to the value of the creep strain rate, which was determined by calculation based on known experimental data. The possibility of determining the transition from the secondary creep to the stage of avalanche-like growth of strains and hidden damage is shown. For the considered example, the location of the channels in the representative image was determined. A technique for correcting creep curves with the involvement of material structure image processing data is proposed.

BIF-Net: Boundary information fusion network for abdominal aortic aneurysm segmentation

The accurate abdominal aortic aneurysm (AAA) segmentation is significant for assisting clinicians in diagnosis and treatment planning. However, existing segmentation methods exhibit a low utilization rate for the semantic information of vessel boundaries, which is disadvantageous for segmenting AAA with significant scale variability of vessel diameter (diameter ranges from 4 mm to 85 mm). To tackle this problem, we introduce a boundary information fusion network (BIF-Net) specially designed for AAA segmentation. BIF-Net initially constructs convolutional kernel functions based on Gabor and Sobel operators, enriching the global semantic features and localization information through the Gabor and Sobel dilated convolution (GSDC) module. Additionally, BIF-Net supplements lost boundary feature information during the sampling process through the guided filtering feature supplementation (GFFS) module and the channel-spatial attention module (CSAM), enhancing the ability to capture targets with shape diversity and boundary features. Finally, we introduce a boundary feature loss function to alleviate the impact of the imbalance between positive and negative samples. The results demonstrate that BIF-Net outperforms current state-of-the-art methods across multiple evaluation metrics, achieving the highest Dice similarity coefficient (DSC) accuracies of 93.29 % and 91.01 % on the preoperative and postoperative datasets, respectively. Compared to the state-of-the-art methods, BIF-Net improves DSC by 6.86 % and 3.85 %. Due to the powerful boundary feature extraction ability, the proposed BIF-Net is a competitive AAA segmentation method exhibiting significant potential for application in diagnosis and treatment processes of AAA.