Image Processing Algorithms Research Articles

Comparison of Image Enhancement Algorithms Based on Denoising and Edge Detection

Image enhancement technology is crucial in many fields, including noise reduction and edge enhancement. This paper compares median filtering and Gaussian filtering for image denoising by practicing. Median filtering removes salt-and-pepper noise well and preserves edges, suitable for discrete noise and detail-critical scenarios like remote sensing and medical imaging. Gaussian filtering smoothes Gaussian and random noise but blurs edges, applicable for background noise reduction. For edge enhancement, Sobel and Laplace operators are contrasted. Sobel operator highlights directional edges, useful in dynamic and noisy scenarios like surveillance. Laplace operator globally sharpens multi-directional edges, sensitive to details but noise, often used in medical image boundary extraction. Their combination improves edge detection. This research provides a solid theoretical foundation for optimizing the application of image enhancement techniques, offering valuable insights and guidance for selecting appropriate image processing algorithms in diverse fields such as medical image processing, autonomous driving, and other advanced technological domains. Future studies could focus on adaptive denoising algorithms to meet diverse image processing needs.

Automated fault detection and analysis for large photovoltaic systems using photovoltaic module fault detection in drone vision system

This study presents an innovative approach to fault detection in large-scale photovoltaic (PV) systems by leveraging the capabilities of drones and machine vision technologies. The proposed method is unsupervised, eliminating the need for manual intervention in identifying and analyzing faults in PV installations. By employing drone vision techniques equipped with high-resolution cameras and advanced image processing algorithms, comprehensive visual data of solar panels were captured. The collected images were processed for automatic detection and classification of various faults such as cracks, hotspots, and shading issues. The integration of these technologies not only enhances the accuracy of fault detection but also significantly reduces the time and cost associated with traditional inspection methods. This approach ensures the efficient and reliable operation of PV systems, contributing to the sustainable generation of solar energy. The original dataset employed in this work can be found at https://doi.org/10.17632/5ssmfpgrpc.1.

CoReSi: a GPU-based software for Compton camera reconstruction and simulation in collimator-free SPECT

Objective.Compton cameras (CCs) are imaging devices that may improve observation of sources ofγphotons. The images are obtained by solving a difficult inverse problem. We present CoReSi, a Compton reconstruction and simulation software implemented in Python and powered by PyTorch to leverage multi-threading and to easily interface with image processing and deep learning algorithms. The code is mainly dedicated to medical imaging and near-field experiments where images are reconstructed in 3D.Approach.The code was developed over several years in C++, with the initial version being proprietary. We have since redesigned and translated it into Python, adding new features to improve its adaptability and performances. This paper reviews the literature on CC mathematical models, explains the implementation strategies we have adopted and presents the features of CoReSi.Main results.The code includes state-of-the-art mathematical models from the literature, from the simplest, which allow limited knowledge of the sources, to more sophisticated ones with a finer description of the physics involved. It offers flexibility in defining the geometry of the CC and the detector materials. Several identical cameras can be considered at arbitrary positions in space. The main functions of the code are dedicated to the computation of the system matrix, leading to the forward and backward projector operators. These are the cornerstones of any image reconstruction algorithm. A simplified Monte Carlo data simulation function is provided to facilitate code development and fast prototyping.Significance.As far as we know, there is no open source code for CC reconstruction, except for MEGAlib, which is mainly dedicated to astronomy applications. This code aims to facilitate research as more and more teams from different communities such as applied mathematics, electrical engineering, physics, medical physics get involved in CC studies. Implementation with PyTorch will also facilitate interfacing with deep learning algorithms.

Towards Smart Farming Approach for Plants Disease Detection and Classification by Using HoG Feature Extraction and PNN Algorithm

The advancement of computerized automated diagnostic technologies ensures that health plants are more effective by detecting diseases early. The damage caused by them can be minimized. Recently, many scientists have been trying to use methods and algorithms in image processing and extracting features based on color, texture, and shape and employing them in building smart systems capable of detecting plant diseases in their early stages. In this paper, proposed a smart farming aims to automated remove shadow and segmentation the pepper fruit diseases region, then extracted strong features from diseases region, and finally detected and diagnosis pepper fruit disease using PNN algorithm. In addition, in this work, (Diyala Pepper), which relies on a dataset of pepper plants named Diyala Pepper. The suggested system's implementation was done on a real dataset of pepper fruits. Experimental results of the proposed method showed 100% accuracy in the training phase and 81.82% in the phase of testing.

Peri-operative Intrastromal Corneal Segmentation after creation of corneal cuts based on Laser Induced Optical Breakdown: A Perspective Study

Significance Assessing refractive correction techniques, such as Lenticule Extraction or LASIK proves instrumental in diagnostics and monitor treatment efficacy. Moreover, these evaluations aid in elucidating potential post-operative complication stemming from imperfect residuals. However, strong noise distribution and low contrast between adjacent faults complicate accurate image segmentation and characteristic inference. This study proposes an image processing algorithm designed to automatically characterize volumes for lenticule extraction or surfaces for flap lift on corneal refractive surgery. Aim The proposed approach fills a current gap in precise reassessment of intrastromal laser cuts where manual expert measurements were seen as the conventional (user-based) standard. Approach The proposed algorithm employs a non-local denoising, the Sobel gradient approach, and Bayesian optimization to delineate and characterize cuts determining their macroscopic parameters. Our analysis involved testing 60 ex-vivo porcine eyes subjected either to flap or lenticule cuts using the SCHWIND ATOS femtosecond laser system. In-situ image analysis was conducted and compared with the intended geometries. Results 60 porcine eyes treated either through flap or lenticule were characterized with our proposed approach exceeding conventional manual measurements. The reproducibility and reliability of the algorithm enabled inferential analysis which might not be achieved with doubted user-base marking. Conclusions Our effort was concentrated on demonstrating the potential of the proposed work, particularly its ability to achieve comprehensive intrastromal characterization—a capability, to the best of our knowledge, being showcased for the first time.

Toward Efficient Edge Detection: A Novel Optimization Method Based on Integral Image Technology and Canny Edge Detection

The traditional SIFT (Scale Invariant Feature Transform) registration algorithm is highly regarded in the field of image processing due to its scale invariance, rotation invariance, and robustness to noise. However, it faces challenges such as a large number of feature points, high computational demand, and poor real-time performance when dealing with large-scale images. A novel optimization method based on integral image technology and canny edge detection is presented in this paper, aiming to maintain the core advantages of the SIFT algorithm while reducing the complexity involved in image registration computations, enhancing the efficiency of the algorithm for real-time image processing, and better adaption to the needs of large-scale image handling. Firstly, Gaussian separation techniques were used to simplify Gaussian filtering, followed by the application of integral image techniques to accelerate the construction of the entire pyramid. Additionally, during the feature point detection phase, an innovative feature point filtering strategy was introduced by combining Canny edge detection with dilation operations alongside the traditional SIFT approach, aiming to reduce the number of feature points and thereby lessen the computational load. The method proposed in this paper takes 0.0134 s for Image type a, 0.0504 s for Image type b, and 0.0212 s for Image type c. In contrast, the traditional method takes 0.1452 s for Image type a, 0.5276 s for Image type b, and 0.2717 s for Image type c, resulting in reductions of 0.1318 s, 0.4772 s, and 0.2505 s, respectively. A series of comparative experiments showed that the time taken to construct the Gaussian pyramid using our proposed method was consistently lower than that required by the traditional method, indicating greater efficiency and stability regardless of image size or type.

Image Processing using VITIS Model Composer

This study outlines a method for image processing utilizing the Vitis Model Composer. AMD's Vitis Model Composer includes comprehensive libraries that support a variety of algorithms. In this work, it is integrated within the MATLAB Simulink environment. A model-based design approach is employed to implement various image processing algorithms. To ensure accuracy, hardware co-simulation is conducted for result verification. The study covers several image processing algorithms, including those for converting RGB images to grayscale, creating image negatives, enhancing images, subtracting backgrounds, applying thresholding, and performing morphological operations such as erosion, dilation, and masking, using AMD's provided blocks. Key Words: optics, photonics, light, lasers, templates, journals

Advanced IoT-integrated parking systems with automated license plate recognition and payment management

Urban parking management is a growing challenge with increasing vehicle numbers and limited parking space. Traditional methods often fail during peak hours, leading to inefficiencies, unauthorized usage, and revenue losses. For instance, a parking lot designed for 300 vehicles often exceeds 90% occupancy during peak times, creating congestion and billing inaccuracies. This research proposes an automated system integrating sensors, image processing, and database management to address these issues. A single camera monitors multiple parking slots, with predefined coordinates linked to IR sensors for dual verification. Image processing algorithms, including Optical Character Recognition (OCR), enable accurate license plate recognition. Testing under real-world conditions showed 95% accuracy in daylight, 90% in low light, and 93% for plates at 45-degree angles. Detection accuracy reached 88% at distances of 1.5–3 m, ensuring reliable operation even at the camera’s range limits. Occupancy tracking achieved less than a 5% error margin compared to manual methods, while the fare calculation module reduced billing errors by 90%, enhancing efficiency and revenue. The system’s scalable design supports applications in parking management, toll collection, and traffic monitoring. By improving vehicle detection, occupancy tracking, and billing accuracy, this solution addresses critical challenges in urban parking and contributes to smarter city infrastructure.

Automatic Sorting and Grading of Fruits Based on Maturity and Size Using Machine Vision and Artificial Intelligence

This paper introduces a computer vision-based system designed for the automated grading and sorting of agricultural products based on their size and maturity. The proposed machine vision system aims to replace traditional manual methods commonly used for sorting and grading fruits. Manual inspection often struggles to ensure consistency in grading and uniformity in sorting. To address these challenges and enhance the quality of fruit grading, image processing and machine learning algorithms can be employed. Key attributes such as the fruit’s shape, color, and size can be analyzed to enable a non-destructive approach to classification and grading. Automation of these processes becomes feasible when standardized criteria for grading are established. Such systems offer faster operations, save time, and reduce manual labor, making them highly valuable to meet the increasing demand for premium-quality agricultural produce.

Machine learning based Obstacle and wild animal detection for avoiding accidents on Railway Tracks

This study focuses on the critical task of detecting wild animals and obstacles in their natural environment. The proposed algorithm leverages deep learning techniques to classify and identify animals and obstacles based on image data, providing an efficient means of monitoring them. Given the diversity of animals and obstacles, manual identification can be challenging, making automation essential. By applying advanced image processing and deep learning algorithms, the system detects potential hazards and alerts train operators, helping to prevent accidents. The approach emphasizes long-range recognition for effective monitoring of wildlife and obstacles in railway environments. Key Words: Long-range recognition, wild animal and obstacle detection, deep learning, image processing.

Online Inspection of Packed Cases

This paper presents a novel approach to automate the inspection process of packed agricultural cases using Artificial Intelligence (AI) and image processing techniques. The current manual inspection system, which assesses only 10% of packed cases based on subjective expert judgment, faces limitations such as inefficiency, subjectivity, and an inability to scale. The proposed solution leverages AI models and advanced image processing algorithms to perform real-time, automated inspections of packed cases. The system assesses quality based on predefined parameters like color, ripeness, and uniformity, as per customer specifications. This approach offers numerous benefits, including increased accuracy, scalability, cost reduction, and enhanced customer satisfaction. Keywords— Artificial Intelligence (AI), Image Processing, Online Inspection, Quality Control Automation, Agricultural Products, Real-Time Feedback, Machine Learning, Objectivity in Inspection, Automated Quality Assessment, Case Inspection.

A New Approach to COVID-19 Detection Using Alexnet From Chest CT Images

Background: At the beginning of 2020, the pneumonia-like COVID-19 virus spread rapidly worldwide. With the emergence of this dangerous virus, work, and daily life have become very difficult. To control this virus, all the centers are closed and quarantined by the government and the countries of the world. Every day, many people from all over the world die due to COVID-19. Many efforts are being made in all fields to diagnose and treat people infected with this virus. For this reason, many researchers started working on identifying this virus and its types. The Scientifics, in computer science, did not sit idle either. Method: In some studies, different image processing methods and algorithms have been used to extract the edge features of the Computed Tomography (CT) images of the lung. In this clinical-computerized study, lung CT images of non-infected and infected people from different hospitals in Lorestan province were used. This collection has 90 stereotypes of images in jpg format of CT scans of people's lungs, each file has more than 200 images from different angles of lung imaging. The preprocessing methods were the first step of the research. In the second phase, edge detection was applied to the dataset to get the highest accuracy rate. Consequently, a classification Convolutional Neural Network (CNN)-AlexNet architecture was used to reach the final aim. Results: The results show that the average accuracy rate of image edge extraction with a threshold value of 0.1 is 93% and the accuracy rate of AlexNet architecture classification is 100%. The proposed method helps physicians to improve disease diagnosis from lung CT images to achieve a more accurate detection rate. Conclusion: This study shows that the CNN-AlexNet architecture effectively increases the diagnosis accuracy rate than the other methods. It is suggested that educational programs for researchers in the field of disease detection from radiology images be provided and that the effectiveness of different types of deep learning methods be compared in future studies.

An ensemble multi-dimensional randomization network for intelligent recognition of tobacco baking stage

In recent years, image processing technology has been increasingly studied on intelligent unmanned platforms, and the differences in the shooting environment during tobacco baking pose challenges to image processing algorithms. To address this problem, an ensemble multi-dimensional randomization network (EMRNet) for intelligent recognition of tobacco baking stage is proposed. The first is to obtain the tobacco leaf area during the baking process. Then, a multi-dimensional randomization network (MRNet) is designed to recognize tobacco baking stage. The effectiveness of MRNet lies in multi-scale hidden layer feature extraction, which can effectively enhance the expression ability of features to overcome the impact of differences between different environments on the tobacco baking stage. Finally, MRNet is used as component learner for constructing an ensemble randomization network structure to distinguish the tobacco baking stage. On the constructed tobacco baking stage dataset, EMRNet achieves 89.14% accuracy with 642.96MFLOPs. Compared with SVM, MLP, BP, ELM, CRVFL and other algorithms, EMRNet shows excellent performance in accuracy and model complexity. The proposed method explores the application of image processing technology in crop baking and drying, providing theoretical support for intelligent baking technology.

A Cooperative game theory-based feature selection for efficient hand grasp classification using minimal number of sEMG signals

Deploying bio-electrical signals and image processing (visual) techniques are the two popular means to provide input to generate grasp control for robotic and prosthetic devices. Visual perception-based techniques rely on computationally expensive image processing algorithms and are affected by lighting conditions. In contrast, grasp control based on bio-electric signals such as surface electromyography (sEMG) is invariant to lighting conditions. It can reflect human intent to hand motion or grasp with lesser computational costs. In this article, we propose an efficient machine learning pipeline to classify hand grasp using a minimal number of sEMG sensors. A cooperative game theory-based feature selection technique is applied to find the representative feature subset. The feature selection method uses a modified marginal contribution based on the class distribution coefficient to generate feature ranking. This feature ranking is further used to find the most representative feature subset from the extracted feature set. Our proposed pipeline has been evaluated on a benchmark dataset and has achieved a classification accuracy of 98.20%, using single-channel EMG when coupled with the XGBoost classifier. Thorough assessments were conducted to confirm the reliability of the results obtained. Our proposed pipeline holds the potential to facilitate the development of cost-effective sEMG prosthetics.

INTELLIGENT BREAST CANCER DIAGNOSIS THROUGH DEEP LEARNING SEGMENTATION

Breast cancer remains one of the most prevalent and deadly forms of cancer worldwide, necessitating innovative approaches for early detection and treatment. The advent of deep learning has revolutionized medical imaging, particularly in the field of breast cancer diagnosis. This research explores the application of deep learning techniques in breast cancer image processing and segmentation. Utilizing convolutional neural networks (CNNs), the study aims to enhance the accuracy and efficiency of identifying malignant tumors in ultrasound images and other breast imaging modalities. By leveraging large datasets and advanced image processing algorithms, deep learning models are trained to differentiate between healthy and cancerous tissues with high precision. This approach not only improves diagnostic capabilities but also aids in the segmentation of tumors, which is crucial for treatment planning and monitoring. The integration of deep learning in breast cancer detection holds significant promise for reducing false positives and negatives, ultimately contributing to better patient outcomes and personalized care. Keywords: Breast cancer, Classification, Segmentation, Artificial intelligence (AI), Machine learning, Deep learning, Texture analysis, Shape analysis

Crackwave R-convolutional neural network: A discrete wavelet transform and deep learning fusion model for underwater dam crack detection

Crack detection is an essential part of structural health monitoring (SHM) for underwater dams, which is crucial for preventing potential structural failures and ensuring the long-term stability. Deep learning-based image processing algorithms have become a research hotspot in the field of crack detection. However, the complex underwater environment has posed challenges to underwater dam crack detection. To address these issues, we propose CrackWave R-convolutional neural network (CW R-CNN), a novel underwater dam crack detection model that fuses discrete wavelet transform (DWT) and deep learning. The proposed model introduces a novel backbone network, DwtResNet, which incorporates DWT to comprehensively extract frequency-domain features from underwater crack images. To overcome the limitations of Intersection over Union (IoU), particularly when predicted and ground truth bounding boxes do not overlap, we employ the generalized IoU (GIoU) function. Furthermore, we apply the soft nonmaximum suppression (NMS) algorithm to reduce the risk of missing fine cracks. In addition, we utilized a self-developed underwater dam image acquisition robot to capture a large number of underwater dam crack images, forming the self-acquired dataset. Evaluating the proposed model on this dataset showed that its MAP_0.5 outperformed SSD, YOLOv5, and the conventional Faster R-CNN. The proposed model proved more effective than other models, especially in detecting fine cracks and handling complex backgrounds. These experimental results not only demonstrate the effectiveness of CW R-CNN in underwater dam crack detection but also highlight its potential application in SHM. It provides essential technical support for the safe monitoring of underwater dam structures.

Application of entertainment interactive robots based on deep learning in referee assistance mode in sports competitions

Application of entertainment interactive robots based on deep learning in referee assistance mode in sports competitions

Application of thermal radiation image processing and efficient facial image restoration algorithm in big data student management

Application of thermal radiation image processing and efficient facial image restoration algorithm in big data student management

Real-time path planning of factory car based on image processing and Dijkstra algorithm

Real-time path planning of factory car based on image processing and Dijkstra algorithm

Piktor-O-Bot: Integrated image processing algorithms for portrait drawing robot applications

Piktor-O-Bot: Integrated image processing algorithms for portrait drawing robot applications