Image Processing System Research Articles

Augmented Reality Enhanced Image Processing Systems for Interactive Worker Training in Industrial Automation

This paper explores the integration of augmented reality (AR) with image processing systems to enhance interactive worker training in industrial automation environments. The proposed system leverages AR technology to overlay real-time visual information, such as instructional graphics and step-by-step guidance, onto physical workspaces, improving the learning experience for workers. By incorporating advanced image processing techniques, the system ensures precise recognition of industrial tools, components and machinery, facilitating context aware training that adapts to the real-time actions of the trainee. This approach aims to accelerate the onboarding process, reduce human error, and increase efficiency in manufacturing settings, providing workers with immersive, hands-on learning opportunities while enhancing safety and operational effectiveness. The paper discusses the design, implementation and potential benefits of AR – enhanced image processing systems in industrial training programs, with a focus on scalability and practical deployment in diverse industrial environments. Keywords— Augmented Reality (AR), Image Processing, algorithms, Interactive Training, Industrial automation.

Image Contrast Enhancement Using General Histogram Equalization and Homomorphic Filtering

The realm of image processing is characterized by the judicious application of mathematical operations to facilitate image transformation and refinement. By synergizing signal processing techniques, image processing can culminate in an enhanced image or the extraction of salient parameters. This research concentrates on ameliorating the contrast of images beset by low contrast, concomitantly extracting relevant image parameters. Images with subpar contrast can engender flawed outcomes in myriad disciplines, highlighting the necessity of contrast enhancement. This study introduces an innovative image processing system, conducting a comparative analysis of General Histogram Equalization and Homomorphic Filtering. The results unequivocally demonstrate the superiority of Homomorphic Filtering. The system's output manifests pronounced efficiency in elevating image contrast, heralding far-reaching implications.

Sign Language Translator via Smartphone Image Analysis using Convolutional Neural Network

This study presents the design and implementation of a smartphone-based image processing system aimed at translating sign language gestures into text in real-time. The primary objective was to create a platform that enhances communication between deaf individuals and non-sign language users through the accurate recognition of sign language gestures. Using Kotlin programming and MediaPipe, the system captures and processes gestures, converting them into readable text. The results demonstrated that the system is capable of recognizing and translating various sign language gestures with a high degree of accuracy and efficiency. The successful deployment of this system highlights its potential to break down communication barriers and offers a foundation for future improvements, including expanded sign language support and performance optimization for real-world applications.

Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection

This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical–classical deep learning architectures within 6G environments.

An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating.

This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO2) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO2 concentration level, and video processing-based pests' detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.

NetraDeep: An Integrated Deep Learning and Image Processing System for Precise Detection of Hard Exudates

Hard exudate (HE) is a common manifestation of various eye diseases, such as diabetic retinopathy (DR), and a prominent cause of vision loss and blindness. Researchers aim to visualize and quantify these exudates using deep learning (DL) and image processing (IP) models from retinal images. However, the requirement for a large number of labelled image datasets for DL models to work on diverse and poor-quality images makes this task challenging. To address this challenge, we introduce NetraDeep, a system that integrates data-driven DL and rule-based IP techniques for exudate segmentation. Our system uses IP models to detect and extract some features and assists DL models in detecting more advanced features and vice versa. The IP models are rule-based and use predefined rules to process images, while the DL models are data-driven and learn from the input data. NetraDeep provides visual and quantitative assessments while mitigating noise and other confounding factors such as artifacts and noise. The training of DL models of this system requires only a limited number of labelled fundus images from publicly available datasets. It provides accurate pixel-wise segmentation results on the public and private image datasets collected from local eye hospitals. Through extensive evaluation, our system achieved remarkable performance, with a dice coefficient of \(0.84\) for the public dataset and a rating of \(9.78\) and \(9.43\) out of 10, as corroborated by two medical experts with experience of more than 20 and 5 years, respectively, for the private image dataset.

Construction of Akushsky Core Functions Without Critical Cores

The residue number system is widely used in cryptography, digital signal processing, image processing systems, and other areas where high-performance computing is required. One of the main tools used in the residue number system is the Akushsky core function. However, its use is limited due to the existence of so-called critical cores. This study aims to develop Akushsky core functions that effectively eliminate the occurrence of critical cores, thereby enhancing their applicability in real-world scenarios. We introduce a fundamental approach to critical core detection that reduces the average time for critical core detection by 99.48% compared to the brute force algorithm. The results of our analysis indicate not only a substantial improvement in the speed of core detection but also an enhancement in the overall performance of systems utilizing the Akushsky core function. Our findings provide important insights into optimizing residue number systems and encourage further exploration into advanced computational techniques within this domain.

Design and Implementation of a Width-Adjustable Asynchronous FIFO for Cross-Clock Domain Data Transmission

Existing research shows that asynchronous FIFO has been widely used in a variety of complex system designs, such as FPGA-based image processing systems, USB communication systems, and so on. To make the designed asynchronous FIFO more programmable and flexible, so that it can adapt to various application scenarios more quickly, this paper studies and designs an asynchronous FIFO with adjustable input and output bit width. In this study, according to the relationship between 24-bit input data and 32-bit storage space, four 24-bit data are selected to fill three RAM storage spaces at one time. According to the multiple relationships between 32-bit storage space and 128-bit output data, the reading pointer is enlarged by 4 times to realize the function of reading four 32-bit data at one time. This whole process realizes the conversion of data bit width through data splicing. Finally, the 24-bit RGB888 image data is successfully converted into 128-bit data and transmitted to the bus, which successfully solves the problem of data width mismatch.

Detecting and evaluating surface defects in additive manufacturing composite materials via image processing technique

Purpose This study aims to offer an image-based robust edge detection system that can estimate, identify, locate and label surface flaws during manufacturing for real-time surface issue diagnostics. Of great concern, this methodology extrapolates surface defect information from scanning electron microscopy (SEM) images of composite fracture surfaces. This study predicts changes in topological intensity of composite fracture surfaces and display them as real-time surface intensity values for the first time. Design/methodology/approach This work, however, introduces a novel robust edge detection method – based image processing – as it is shown to be effective in locating defects, as measured by SEM images of composite fracture surfaces created using additive manufacturing (AM). SEM images, obtained in this study, are related to previous study considering the fracture surfaces of reinforced thermoset composites created via the AM method. These SEM images are of two types: fracture surface of AM of carbon fiber reinforced thermoset composites and fracture surface of AM of syntactic foam reinforced thermoset composites. Initially, MATLAB environment is used for analyzing the SEM images; the technique used, as well as the validity are explained more in the methodology section. Findings The robust surface defect inspection approach used herein is found to be capable of predicting, identifying, localizing and labeling surface defects during production, allowing for real-time surface issue diagnosis. Further, this work makes it possible to use image processing and analysis of these surfaces to anticipate fluctuations in the topological intensity of the fracture surfaces of composites and represent them as values of surface intensity in real time. Originality/value Rising worldwide company rivalry requires a fast, accurate component failure diagnostic method. To create an efficient feature set, a surface defect inspection system must identify product flaws in real time. Thus, this study proposed an image-based robust edge detection system – based on MATLAB environment – that is capable of estimating, identifying, locating and labeling surface faults during production. This paves the way for an extensive set of high-quality tools for dealing with a wide range of problems associated with digital image processing in composites. As a result, the ability to define methodologies and rapidly prototype prospective solutions typically minimizes the cost and time required to implement a successful system during the design phase of an image processing system.

On-Orbit Geometric Calibration and Accuracy Validation of the Jilin1-KF01B Wide-Field Camera

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km swath width and 0.5-m resolution data. To ensure the geometric accuracy of high-resolution image data, the GCGC method conducts grouped calibration of the time delay integration charge-coupled device (TDI CCD). Each group independently calibrates the exterior orientation elements to address the multi-time synchronization issues between imaging processing system (IPS). An additional inter-chip geometric positioning consistency constraint is used to enhance geometric positioning consistency in the overlapping areas between adjacent CCDs. By combining image simulation techniques associated with spectral bands, the calibrated panchromatic data are used to generate simulated multispectral reference band image as control data, thereby enhancing the geometric alignment consistency between panchromatic and multispectral data. Experimental results show that the average seamless stitching accuracy of the basic products after calibration is better than 0.6 pixels, the positioning accuracy without ground control points(GCPs) is better than 20 m, the band-to-band registration accuracy is better than 0.3 pixels, the average geometric alignment consistency between panchromatic and multispectral data are better than 0.25 multispectral pixels, the geometric accuracy with GCPs is better than 2.1 m, and the geometric alignment consistency accuracy of multi-temporal data are better than 2 m. The GCGC method significantly improves the quality of image data from the Jilin1-KF01B satellite and provide important references and practical experience for the geometric calibration of other large-swath high-resolution remote sensing satellites.

ESPressoscope: A small and powerful approach for in situ microscopy.

Microscopy is essential for detecting, identifying, analyzing, and measuring small objects. Access to modern microscopy equipment is crucial for scientific research, especially in the biomedical and analytical sciences. However, the high cost of equipment, limited availability of parts, and challenges associated with transporting equipment often limit the accessibility and operational capabilities of these tools, particularly in field sites and other remote or resource-limited settings. Thus, there is a need for affordable and accessible alternatives to traditional microscopy systems. We address this challenge by investigating the feasibility of using a simple microcontroller board not only as a portable and field-ready digital microscope, but furthermore as a versatile platform which can easily be adapted to a variety of imaging applications. By adding a few external components, we demonstrate that a low-cost ESP32 camera board can be used to build an autonomous in situ platform for digital time-lapse imaging of cells. Our prototype of this approach, which we call ESPressoscope, can be adapted to applications ranging from monitoring incubator cell cultures in the lab to observing ecological phenomena in the sea, and it can be adapted for other techniques such as microfluidics or spectrophotometry. Our prototype of the ESPressoscope concept achieves a low power consumption and small size, which makes it ideal for field research in environments and applications where microscopy was previously infeasible. Its Wi-Fi connectivity enables integration with external image processing and storage systems, including on cloud platforms when internet access is available. Finally, we present several web browser-based tools to help users operate and manage our prototype's software. Our findings demonstrate the potential for low-cost, portable microscopy solutions to enable new and more accessible experiments for biological and analytical applications.

Advanced dual-input artificial optical synapse for recognition and generative neural network

Perovskite materials have emerged as leading candidates for optical synaptic devices due to their superior photosensitivity and tunable optoelectronic properties. However, the practical application of perovskite-based optoelectronic synaptic transistors has been hindered by issues of poor stability and high toxicity. This study developed an artificial synaptic thin film transistor (TFT) based on a Cs2AgBiBr6/InOx bilayer. The device demonstrated synaptic behavior under optoelectronic hybrid stimulation (largest wavelength ∼520 nm), such as excitatory postsynaptic current (EPSC), inhibitory postsynaptic current (IPSC), paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), short-term memory (STM) and long-term memory (LTM). Notably, the “light writing and voltage erasing” characteristics of these devices could be utilized to construct a convolutional neural network (CNN) classifier for the CIFAR-10 dataset, demonstrating noise tolerance close to the human eye. The loss in recognition accuracy was within 1 % when Gaussian white noise and salt pepper noise were added. Furthermore, these devices exhibited great potential in cycle-consistent generative adversarial networks (CycleGAN), with the generated image quality achieving levels of 0.637 and 0.715 in improved perceptual image processing system (IPIPS) and structural similarity index measure (SSIM) evaluation metrics for the zebra dataset, indicating good image quality. This study indicates that our Cs2AgBiBr6-based artificial optical synaptic TFTs are promising for sensing and in-memory computing applications.

A systematic investigation of the freezing process of silty clay through unidirectional stepwise freezing method combined with digital imaging processing technology

Soil freezing represents a complex thermo-hydro-mechanical coupling process that encompasses a range of physical and mechanical phenomena, such as heat transfer, moisture migration, cryostructure development, ice lens segregation, frost heave, and consolidation, all of which interact and influence one another throughout the freezing process. This study conducted a unidirectional stepwise freezing test, comprising three distinct freezing stages, to investigate the freezing behavior of Qinghai-Tibet silty clay using digital imaging processing technology (DIPT), which includes an image acquisition system and a digital image processing system. The results demonstrate that the stepwise freezing test provides a more comprehensive insight into the physical and mechanical processes occurring during unidirectional soil freezing. At various stages of freezing, the temperature distribution within the sample achieves a linear stabilization, with the frozen zone exhibiting a slightly larger extent compared to the unfrozen zone. The longitudinal section of the sample developed layered cryogenic cracks, whereas the horizontal section revealed interconnected cryogenic polygonal cracks of varying sizes, which facilitated moisture migration and ice segregation. Furthermore, ice lenses can segregate within a broad temperature range below the soil freezing point in the frozen zone, resulting in frost heave within the frozen zone and consolidation of the unfrozen zone. This study proposes an experimental method to clarify the complex phenomenon and mechanisms of frost heave during soil freezing.

Pathfinding Pulsar Observations with the CVN Incorporating the FAST

Abstract The importance of Very Long Baseline Interferometry (VLBI) for pulsar research is becoming increasingly prominent and receiving more and more attention. We present the pathfinding pulsar observation results with the Chinese VLBI Network (CVN) incorporating the Five-hundred-meter Aperture Spherical radio Telescope (FAST). On MJD 60045 (11th April 2023), PSRs B0919+06 and B1133+16 were observed with the phase-referencing mode in the L-band using four radio telescopes (FAST, TianMa, Haoping, and Nanshan) and correlated with the pulsar binning mode of the distributed FX-style software correlator in Shanghai. After further data processing with the NRAO Astronomical Image Processing System (AIPS), we detected these two pulsars and fitted their current positions with accuracy at the milliarcsecond level. By comparison, our results show significantly better agreement with predicted values based on historical VLBI observations than those with previous timing observations, as pulsar astrometry with the VLBI provides a more direct and model-independent method for accurately obtaining related parameters.

Integration of Payload Sensors to Enhance UAV-Based Spraying

This work focuses on the use of load sensors to help with spraying tasks using unmanned aerial vehicles (UAVs). The study details the construction of a prototype for load measurement to validate the proof of concept. To simulate the application of agricultural pesticides, the UAV follows a predefined route and an image processing system detects the presence of diseased plants. After detection, the UAV pauses its route momentarily and activates the spraying device. The payload sensor monitors the fertilizer application process, which determines whether the amount of pesticide has been fully applied. If the storage tank is empty or the remaining quantity is insufficient for another operation, the system will command the UAV to return to the base station for refueling. Experimental validations were carried out in an indoor controlled environment to verify the proposal and the functionality of the in-flight payload monitoring system. Additionally, the UAV’s flight controller demonstrated robust performance, maintaining stability despite the challenges posed by liquid-load oscillations and varying payloads during the spraying process. In summary, our main contribution is a real-time payload monitoring system that monitors weight during flight to avoid over- or under-spraying. In addition, this system supports automatic refueling, detecting low levels of pesticides and directing the UAV to return to base when necessary.

Multi-Sensing Inspection System for Thermally Induced Micro-Instability in Metal-Based Selective Laser Melting.

Additive manufacturing (AM) excels in engineering intricate shapes, pioneering functional components, and lightweight structures. Nevertheless, components fabricated through AM often manifest elevated residual stresses and a myriad of thermally induced micro-instabilities, including cracking, incomplete fusion, crazing, porosity, spheroidization, and inclusions. In response, this study proposed a sophisticated multi-sensing inspection system specifically tailored for the inspection of thermally induced micro-instabilities at the micro-nano scale. Simulation results substantiate that the modulation transfer function (MTF) values for each field of view in both visible and infrared optical channels surpass the benchmark of 0.3, ensuring imaging fidelity conducive to meticulous examination. Furthermore, the innovative system can discern and accurately capture data pertaining to thermally induced micro-instabilities across visible and infrared spectra, seamlessly integrating this information into a backend image processing system within operational parameters of a 380-450 mm distance and a 20-70 °C temperature range. Notably, the system's design is harmoniously aligned with the requisites of processing and assembly, heralding a significant advancement in bolstering the inspection effect of thermally induced micro-instabilities for the AM component.

Research on the expansion, shrinkage properties and fracture evolution of red clay stabilised with phosphogypsum under dry-wet cycles.

In view of the special engineering properties of red clay and the waste of phosphogypsum resources, the expansion and contraction deformation and fissure evolution of phosphogypsum stabilized red clay under different conditions were investigated by laboratory tests and image processing system. The research results show that: (1) the absolute expansion and absolute shrinkage of phosphogypsum stabilized red clay are positively correlated with the compaction degree, the number of dry and wet cycles and the cement dosage, and negatively correlated with the initial water content and the phosphogypsum dosage; (2) the fissure rate increases with the increase of the number of dry and wet cycles, and decreases with the increase of the initial water content, the compaction degree, the cement, and the phosphogypsum dosage; (3) The relationship among absolute expansion rate (absolute shrinkage), degree of compaction and fracture rate can be fitted by the equation f(x,y) = ax+by+cx2+dy2+e; (4) Phosphogypsum has an obvious inhibiting effect on the expansion, shrinkage and cracking of the mix. It is recommended that the cement mixing amount of 6% and phosphogypsum: red clay = 1:1~1:2 as roadbed filler.

Advanced image analysis for medical diagnostics: a system for segmentation and classification using level set methods and AI algorithms

This work aims to implement and utilize an advanced computer system for image analysis and processing through artificial intelligence. The system will evaluate images from multiple sources. As a result, a comprehensive e-Medicus system will be developed to capture and analyze X-ray data and classify cancer cells. This innovative tool, with its unique features tailored for medical facilities, will assist them in capturing and analyzing X-ray images and CT scan results. The e-Medicus system offers several benefits for medical facilities, including efficient automation of photo analysis, tracking changes in a patient's condition over time, and facilitating the identification of medical changes and data classification. A multimedia presentation of the change process will use the contour set function, allowing for topological changes in solution properties. The system integrates novel procedures and algorithms from theoretical computer science and numerical mathematics, leveraging neural networks, genetic algorithms, semantic networks, image ontology, rough set theory, contour set methods, and hybrid algorithms. These developed algorithms enhance methods and concepts in image segmentation, gathering, transmitting, storing, extracting, and effectively presenting information.

Design of an image processing system for heterogeneous computer architectures

Embedded real-time image processing systems are widely applied in various fields, enhancing visual experience and making life more convenient. With the development of embedded processors and operating systems, as well as in-depth research into image processing algorithms, the effects and speed of image processing have been significantly improved. This paper proposes an embedded image processing system design based on the current technological developments, centered around the Zybo processor. This includes a collaborative hardware and software design, specific hardware circuit design, and the design of a system software platform. The paper also discusses the establishment of a development environment for the Zybo system, the transplantation of the Linux operating system, the design of the file system, and the application of OpenCV library functions in image processing.

Performance and energy evaluation of dynamic adaptive deterministic routing algorithm for multicore architectures

Chip Multiprocessors (CMPs) leverage multiple processing units to improve computational speed and efficiency. Routing algorithms in NoC (Network-on-Chip) architectures ensure efficient data communication between these units, addressing challenges such as congestion, latency, and power consumption. This paper delves into an extensive analysis of the performance evaluation concerning the Dynamic Adaptive Deterministic (DyAD) routing for Network-on-Chip (NoC) and Wireless Network-on-Chip (WiNoC) configurations within the framework of a 64-core and 100-core multicore architecture. The evaluation involved a congestion threshold analysis of data transmission latency, the efficiency of network data throughput, and the amount of energy consumed. To substantiate our conclusions, we conducted simulations of the 64-core and 100-core mesh-based NoC and WiNoC architectures under the Video Image Processing System (VIPS) benchmark workload. These simulations were executed utilizing the Noxim simulator, a well-recognized tool acclaimed for its capacity to provide cycle-accurate simulations. Analyzing the simulation outcomes, it becomes evident that the DyAD routing approach for the 64-core and 100-core WiNoC architecture performs better in terms of network performance. It is characterized by enhanced capabilities at higher Packet Injection Rate (PIR) saturation loads, improved network throughput, and minimized latencies. This dominance is evident from its ability to handle heavier workloads and achieve lower delays in the investigated VIPS workload situations, compared to the 64-core and 100-core NoC multicore architecture.