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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Deep Learning Applications in Microscopy and Holography for near-field Signal processing 13

Deep Learning Applications in Microscopy and Holography for ...

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13th IEEE International conference on Communication systems and Network Technologies, CSNT 2024

作者： Mohseni, Milad Thilagham, K.T. Aravinda, K.A. Kumar, B Santhosh Nagpal, Amandeep Geetha, B.T. Islamic Azad University Department of Computer and Engineering Tabriz Branch Tabriz Iran Government College of Engineering. Salem Metallurgical Engineering Tamilnadu India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Saveetha School of Engineering Simats Department of Ece Tamilnadu India

ISBN: (纸本)9798350305463

Focusing on near-field signal processing, we provide a novel and comprehensive strategy for improving picture quality, obtaining super-resolution, and performing quantitative analysis in the disciplines of microscopy and holography. Three deep learning algorithms - Enhanced imageNet (EINet), HoloReconGAN, and QuantSegNet - are combined in this method to maximize their potential. In order to improve images, super-resolve them, and analyze them quantitatively, many algorithms have been developed. In order to better comprehend each method, mathematical equations are provided to describe the main steps involved. With the use of convolutional neural networks, noise is reduced and finer features are brought into focus with the help of the Enhanced imageNet (EINet) method. To accomplish super-resolution and 3D reconstruction from holographic data, HoloReconGAN combines generative adversarial networks (GANs) with variational autoencoders (VAEs). Label-free segmentation and quantitative analysis of structures in microscopy and holography pictures are the focus of QuantSegNet, a semantic segmentation network. Our suggested technique has been shown to outperform six established methods over a broad variety of assessment criteria. image quality, noise suppression, feature recognition, computational efficiency, and resilience are just few of the areas where it shines. It's also faster, uses less memory, is more accurate, easier to use, cheaper, and faster. Applications in several scientific fields might be facilitated, and the area of microscopy and holography as a whole could see significant advancements as a result. © 2024 IEEE.

关键词： image enhancement

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image Based Security System in cloud resource Scheduling 5

Image Based Security System in cloud resource Scheduling

引用

5th International conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Chakraborty, Koushik Dora Pravina, C.T. Arya, Nancy Sharma, Sunil Rakesh, Shanu Kuttan Singh, Arvind Kunar Adamas University Kolkata India Department of Mathematics Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi India Department of Computer Science and Engineering Shree Guru Gobind Singh Tricentenary University Gurugram India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Department of Computer Science and Engineering Chouksey Engineering College Bilaspur Chhattisgarh India Department of Computer Science and Engineering S.V.N.I.E.T Barabanki India

ISBN: (纸本)9798400709418

The popularity of cloud computing in scheduling work processes, particularly logical work processes, is growing. The cloud computing environment may encounter significant issues with execution time and cost during resource allocation, resulting in disruptions in customer support quality. Text Passwords are right now a totally utilized insistence part, however graphical passwords and biometrics are likewise utilized. However, we really want to improve both comfort and security in any system to make it extremely secure. image-based security systems leverage visual information and image processing techniques to enhance security measures. These systems use images or video streams from cameras to monitor, detect, and respond to security threats. Most likely, the framework employs our algorithm to improve performance. It utilizes parts of both Graphical and Text Secret phrase plans. Advanced image processing algorithms analyze captured images for various security-related purposes, such as object detection, facial recognition, and anomaly detection. In contrast to the most well-known scheduling algorithms, such as multi-objective scheduling (MOS) and cost-conscious scheduling, the various scheduling algorithms in this proposed hybrid algorithm mannequin are used to make cloud services secure and highly available to end users. Identifying and verifying individuals based on facial features. This is used for access control, identity verification, and tracking individuals of interest. Analyzing images to detect unauthorized persons or objects in restricted areas, triggering alerts or alarms. In this investigation, we attempt to demonstrate the execution of various algorithm systems for the most well-known three static booking calculations. All the calculations are working out the all-out execution cost and timing of various calculations. © 2023 ACM.

关键词： Security systems

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Research on Radar Imaging Method Based on Tensor Neural Network 16

Research on Radar Imaging Method Based on Tensor Neural Netw...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Qiu, Tian Liu, Nan Yang, Minglei National Key Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9781510689251

LFM signals, as the most commonly used form of signals in radar systems, are of significant research value. At this stage, compressed sensing (CS) has significant potential to improve the image quality of radar imaging. However, the existing CS theories have limited ability to improve the imaging capability of LFM signals, and always suffer from low imaging efficiency, parameters need to be manually adjusted, and poor suppression of side flaps. In this paper, we propose a neural network imaging algorithm based on tensor compressed perception, called 2D-ISTA-Net. this algorithm accomplishes distance-azimuth 2D radar imaging by combining a model-based iterative threshold shrinkage (ISTA) algorithm and a data-driven deep learning model. First, the sparse recovery model of the LFM signal is obtained analytically, and then, the 2D-ISTA optimization algorithm is unfolded into a deep network structure in which all parameters are optimized based on data-driven auto-learning instead of manual tuning. Subsequently, the network learning is trained on the scattering point images in the training set to obtain a 2D-ISTA-Net network with autonomous parameter tuning capability. Furthermore, we compared this algorithm with some existing CS algorithms. The data show that 2D-ISTA-Net significantly improves the computing power and has a lower side flap level compared with the traditional CS algorithm. Experiments show that 2D-ISTA-Net has good reconstruction performance. © 2025 SPIE.

关键词： Radar imaging

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A multi-dimension information processing method for low-power forest surveillance systems 3

A multi-dimension information processing method for low-powe...

引用

2024 3rd International conference on Aerospace and Control Engineering, ICoACE 2024

作者： Wan, Zijing Tianjin Fire Science and Technology Research Institute MEM Weijinnan Road No. 110 Tianjin Nankai300170 China

To identify early-stage forest fires using ground-based sensing systems, this paper studies the radiation characteristics of smoke particles and interference particles under natural light in different environments based on a rapid dimensional reduction algorithm of forest monitoring images and a multi-dimensional time series signal graph model. It has completed the forest fire classification and spread prediction method based on multi-sensor and image sequence signal abnormal event detection, which enables the forest fire detection system to achieve comprehensive and efficient collection of fire information at a lower cost and indirectly reduces sensor power consumption and bandwidth. The results can provide quantifiable solutions for forest fire detection and disaster situation assessment and are suitable for the comprehensive promotion of on-site sensing systems. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Fire detectors

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Implementation of an Efficient image Inpainting Algorithm using Optimization Techniques 6th

Implementation of an Efficient Image Inpainting Algorithm us...

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6th International conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Revathi, K. Janardhana Rao, B. Odugu, Venkata Krishna Gade, Harish Babu Sphoorthy Engineering College Hyderabad India CVR College of Engineering Hyderabad India

ISBN: (纸本)9789819984503

To repair the demolished images and remove the particular unnecessary objects in the image, optimized image inpainting techniques are required. In this work, a novel exemplar-based image inpainting technique is suggested. In this technique, the patch priority is computed using the regulation factor and coefficients. Two optimization techniques as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques are employed to attain the optimal values of regularization factor and coefficients. The best exemplar patch selection is carried out by calculating the sum of the absolute difference between the patches. Performance measures including peak-signal-to-noise ratio (PSNR), mean square error (MSE), and Structural Similarity Index (SSIM) are tested using the suggested image inpainting process on images based on datasets. These results are compared with the available inpainting methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic algorithms

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image Reconstruction Leveraging Time-Event Feature Fusion for Event Cameras 7

Image Reconstruction Leveraging Time-Event Feature Fusion fo...

引用

7th International conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Jian Wu, Sheng Liu, Wenwei Feng, Hui Hu, Bo School of Information Science and Technology Fudan University Shanghai200433 China Shanghai Institute of Intelligent Electronics & Systems Shanghai200433 China

ISBN: (纸本)9798350350890

The event cameras generate asynchronous event sequences, rendering most existing image-based algorithms in-applicable for direct use and processing. Therefore, the development of a simulator that utilizes event streams captured by an event camera as input and reconstructs image sequences as output is crucial for subsequent tasks such as high-speed moving object tracking, object recognition and detection, and SLAM. Despite the extensive research and attention garnered by deep learning-based image sequence generation, there remains a problem of local representation information loss during the reconstruction process from event streams to image sequences. Existing solutions, whether based on Stacking Based on Time (SBT) or Stacking Based on Events (SBE), exhibit significant limitations when using a single stacking approach. To address the insufficiency in feature representation capability, this paper proposes a visual image conversion method based on multi-view feature fusion, effectively transforming event streams into traditional image sequences. Extensive experiments and dataset validations, utilizing multiple evaluation metrics including both global and local assessments, demonstrate the advantages of our proposed method. © 2024 IEEE.

关键词： image reconstruction

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Deep Learning-Based Noise Reduction Techniques in Electronic Signal processing 9

Deep Learning-Based Noise Reduction Techniques in Electronic...

引用

9th International conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Johari, Lalit Ramya, P. Suganya, M. Praveena, Hirald Dwaraka Lathamageshwari, P.S. Rajendiran, M. Iftm University Department of Computer Applications Uttar Pradesh Moradabad244102 India Bannari Amman Institute of Technology Department of Electronics And Communication Engineering Tamil Nadu Sathyamangalam638401 India Annapoorana Engineering College Department of Electronics And Communication Engineering Tamil Nadu Salem636308 India Department of Electronics And Communication Engineering Andhra Pradesh Tirupati517102 India Tamil Nadu Kavaraipettai601206 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

This paper explores the operation of deep literacy- grounded noise reduction ways in electronic signal processing. Noise hindrance poses a significant challenge in colorful signal processing operations, impacting the delicacy and trustability of data analysis and interpretation. Traditional noise reduction styles frequently calculate on heuristic approaches or signal processing algorithms, which may have limited effectiveness, especially in complex and dynamic surroundings. In discrepancy, deep literacy algorithms, similar as convolutional neural networks(CNNs) and intermittent neural networks(RNNs), offer promising results for noise reduction by learning complex patterns and connections directly from data. This paper determines the state- of- the- art deep literacy- grounded noise reduction ways, including denoising autoencoders, generative inimical networks(GANs), and intermittent neural network- grounded models. Case studies and experimental results demonstrate the effectiveness and performance of these ways in colorful signal processing tasks, similar as audio denoising, image restoration, and biomedical signal processing. The integration of deep literacy- grounded noise reduction styles into electronic signal processing systems shows significant eventuality for perfecting signal quality and enhancing the delicacy of data analysis in different operations. © 2024 IEEE.

关键词： Recurrent neural networks

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Generalized Minkowski Distance-Based Local Mean k-Nearest Neighbor Classifier

Generalized Minkowski Distance-Based Local Mean k-Nearest Ne...

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2023 IEEE International conference on image processing and Computer Applications, ICIPCA 2023

作者： Sun, Guohao Sun, Yana Luo, Fenfen School of Information Science and Engineering University of Jinan Jinan China School of Electronic Engineering Xi'an Shiyou University Xi'an China College of New Engineering and Industry Putian University Putian China

ISBN: (纸本)9798350314670

This research focuses on comparing the classification performance of Local Mean-based K Nearest Neighbors (LMKNN) using different distance metrics. Specifically, we examine different value of close neighbors and 10 distance metrics within the Lp Norm ranging from 1 to 10, such as Euclidean distance, Minkowski distance and Manhattan distance. The KNN algorithm is a powerful tool for classification and regression tasks. Unlike many other algorithms, KNN is non-parametric, meaning it can adapt to complex models without making any assumptions about the data distribution. This makes it a popular choice for researchers who prefer simple classification model rather than complex one. However, calculating the distance between each test data point and all training data points can be computationally expensive, particularly when working with a large data set. To address this issue, we have consulted an improved KNN algorithm based on the local mean, which significantly reduces the calculation complexity without sacrificing accuracy. We have applied our algorithm to several commonly used data sets on UCI and observed promising classification performance. Furthermore, we have extended the Euclidean distance measurement in the original algorithm to the level of vector norm. This expands the classification index of KNN beyond just Euclidean distance and provides more accurate and robust results. © 2023 IEEE.

关键词： Classification (of information)

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DDM-YOLOv8s for Small Object Detection in Remote Sensing images 7

DDM-YOLOv8s for Small Object Detection in Remote Sensing Ima...

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7th International conference on Machine Learning and Natural Language processing, MLNLP 2024

作者： xi, Yangli Qu, Dan Du, Liming Zhengzhou University Dept. School of Cyberspace Security Henan Zhengzhou China Information Engineering University Dept. School of Information Systems Engineering Henan Zhengzhou China

ISBN: (纸本)9798350354973

In order to solve the problem of low accuracy of remote sensing target detection caused by insufficient feature representation, complex background and diversified scales in remote sensing image target detection task, a remote sensing image target detection algorithm based on DDM-YOLOv8s (DySample DASI-MODSConvYOLOv8s) model was proposed. First, a super lightweight and efficient dynamic upper sampler DySample is used to improve the image resolution of the algorithm without adding additional burden. Secondly, the new C2f_DASI module is constructed by using the dimensional awareness selective integration module DASI and C2f, and the adaptive channel selection and fusion are realized in the feature fusion module, which effectively solves the complex background problem in remote sensing images;Finally, the head part is redesigned by Maintaining the Original information - Depth Separable Convolution MODSConv, which maintains as much feature layer information as possible before prediction, so that the extracted features contain more correlation information, greatly improving the detection accuracy and speed. The experimental results on RSOD datasets show that the test mAP of the improved YOLOv8s algorithm is 94.5%, which proves that this method can effectively improve the detection accuracy of small targets in remote sensing images. © 2024 IEEE.

关键词： Target tracking

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A Centroid Locating Method for Star Sensor Based on Improved Gaussian Surface Fitting 23

A Centroid Locating Method for Star Sensor Based on Improved...

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15th International conference on Digital image processing, ICDIP 2023

作者： Ma, Junyong Zhang, Shengwei Yang, xi Li, Guoqiang National Key Laboratory of Space Based Information Perception and Fusion Luoyang Institute of Electro-optical Equipment Avic China Station of Quality Supervision and Checkout Hubei Province Wuhan China

ISBN: (纸本)9798400708237

As a high precision attitude measurement device, star sensors have been widely used in various types of aircraft. Among many factors that affect the attitude measurement accuracy of star sensors, the accuracy of centroid positioning directly affects the accuracy of star map recognition and attitude resolution. In this paper, an improved Gaussian surface fitting method is proposed. Firstly, an improved cross projection method is used to extract star points. Then a simplified Gaussian surface fitting method is adopted to reduce the complexity of the traditional Gaussian surface fitting algorithm. By this algorithm combination, the efficiency and accuracy of the centroid positioning of the stars can be improved at the same time. A series of simulation star graphs are generated by the Gaussian point spread function imaging model, and the centroid positioning accuracy of the traditional centroid locating algorithms and the improved Gaussian surface fitting algorithm are evaluated. Experiments show that the algorithm is simple and easy to implement and the computation amount is small. As a result, the real-time processing of video image information can be achieved, and the positioning accuracy of centroid can be effectively improved. © 2023 ACM.

关键词： Gaussian distribution

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