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

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An Improved Blind Deconvolution for Restoration of Blurred images Using Ringing Removal processing 4th

An Improved Blind Deconvolution for Restoration of Blurred I...

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4th International conference on Computer and Communication Technologies, IC3T 2022

作者： Dimlo, U. M. Fernandes Narasimharao, Jonnadula Laxmaiah, Bagam Srinath, E. Rani, D. Sandhya Sandhyarani Kumar, Voruganti Naresh Department of CSE Sreyas Institute of Engineering and Technology Telangana Hyderabad India Department of CSE CMR Technical Campus Telangana Hyderabad India Department of CSE Keshav Memorial Institute of Technology UGC Autonomous Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India

ISBN: (纸本)9789811985621

One of the most difficult challenges in image processing is restoring a defocused image by reducing blur and noise. Blurring characterizes image deterioration, and recovery is accomplished by point spread function estimation and ideal image estimation processing was repeated. Ringing, or wavelike artifacts that arise along strong edges, is a difficult challenge in latent image restoration. Therefore, this paper will introduce an improved blind deconvolution for restoring blurry images using ringing removal process. This paper provides an improved deconvolution technique that uses blur kernel prediction based on dark channels before achieving clear image recovery. To hold picture information and beautify the rims of the ringing impact created at some point of the authentic clean picture recuperation process, an easy bilateral clear out is used. The ringing removal method L0 regularization is used with the restoration process, which can estimate a sharper image. By removing the difference map from the final deconvolution result, it is possible to get a clearer picture without ringing. Finally, the results are presented in terms of performance parameters such as signal-to-noise ratio (SNR), mean squared error (MSE), and peak signal-to-noise ratio (PSNR).The results show that the performance parameters of the improved blind deconvolution model are superior compared to existing image blur removal algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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Galois Fields for Digital image and Signal processing: Evidence for the Importance of Field Specificity 5

Galois Fields for Digital Image and Signal Processing: Evide...

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5th International conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Vitulyova, Yelizaveta S. Bakirov, Akhat S. Suleimenov, Ibragim E. Almaty University of Power Engineering and Telecommunications Named after Gumarbek Daukeyev Almaty Kazakhstan National Engineering Academy of the Republic of Kazakhstan Almaty Kazakhstan

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

It is shown that any digital signal and image processing algorithms can be reduced to multivalued logic operations. The basis for this is signal models, in which each discrete level is associated with an element of the Galois field. Therefore, the development of algorithms and computing systems operating directly in Galois fields is topical. To simplify the calculation of products in Galois fields, in turn, it is convenient to pass from an element of the Galois field to the degree of a primitive element that defines this element. This allows the multiplication operation to be reduced to the addition operation. A specific example of an algorithm and a block diagram of a device that implements it, which allows such a reduction, are presented. This example demonstrates that for solving problems of digital signal and image processing, it is very important to take into account the specifics of specific Galois fields. © 2022 IEEE.

关键词： Adders

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A SPATIAL REGISTRATION ALGORITHM FOR NETWORKED RADAR BASED ON GRIDS MERGING

A SPATIAL REGISTRATION ALGORITHM FOR NETWORKED RADAR BASED O...

引用

IET International Radar conference 2023, IRC 2023

作者： Gu, Fengdeng Gao, Chang Zhang, Qingfu Yan, Junkun Jia, Tianyi Liu, Hongwei National Key Laboratory of Radar Signal Processing Xidian University Xi'an710071 China City University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781839539954

The spatial registration for networked radar is an important prerequisite to ensure the accurate implementation of fusion detection by associating the observations of the same target observed by different radar stations. Aiming at the problems of large registration errors and heavy computing burdens in current spatial registration algorithms, a method of grids merging for spatial registration is proposed in this paper. In this method, an optimization model concerning the trade-off between registration performance and computing burdens is established. The Fermat point is obtained as the mark point of grids to be merged, optimising the grid's size and the registration errors. The results of the proposed registration method can be intuitively shown in the forms of the off-line spatial registration information table, the registered position distribution map and the reconstructed overlapping regions of spatial sampling cells distribution map. The reliability of the proposed algorithm is also shown through some simulation results about registration under different conditions. © The Institution of Engineering & Technology 2023.

关键词： image registration

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Auto-Detection of Handwritten Text Documents using Improved image processing Technique 15

Auto-Detection of Handwritten Text Documents using Improved ...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Srinivasan, M. Radha, K. Priya, V. Basha, Shaik Mahaboob Ravi, S. Bannari Amman Institute of Technology Department of Eee Sathyamangalam India Dr.Mahalingam College of Engineering and Technology Department of Cse Coimbatore Pollachi India Department of Microbiology TamilNadu Chennai India School of Engineering Department of Ece Andhra Pradesh Tirupati India Saveetha University Saveetha School of Engineering Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798350370249

Automobile detection of handwritten text files is vital in document control systems. It includes the process of automatically figuring out and extracting handwritten text from pictures and converting it into editable virtual text. This method is challenging due to the variations in handwriting styles and the complexity of handwritten textual content reputation. Current methods for auto-detection depend upon conventional picture processing strategies, including thresholding, segmentation, and characteristic extraction, which need to be more robust to handle various handwriting patterns. A stepped-forward photograph processing method for automobile detection of handwritten textual content documents is proposed to deal with this issue. The technique uses deep mastering algorithms, including convolutional neural networks (CNNs), to robotically research the features of handwritten textual content and classify it appropriately. The proposed technique additionally employs facts augmentation techniques to boost the dataset's variety, making it more robust for variations in handwriting styles. Experimental consequences on an extensive handwritten textual content file dataset show that the proposed technique outperforms traditional techniques, accomplishing a better accuracy fee. The advantages of this method encompass its adaptability to one-of-a-kind handwriting styles, its velocity, and its potential to deal with handwritten textual content in numerous languages © 2024 IEEE.

关键词： Appropriately Augmentation Consequences Experimental Handwritten Mastering

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6th International conference on Smart Computing and Informatics, SCI 2024

6th International Conference on Smart Computing and Informat...

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6th International conference on Smart Computing and Informatics, SCI 2024

ISBN: (纸本)9789819619801

The proceedings contain 46 papers. The special focus in this conference is on Smart Computing and Informatics. The topics include: Resilient Domain Authentication Framework for Enhancing Digital Identity Security;traffic Sign Detection with Pattern Recognition Techniques Using image processing;exploring Advanced Techniques in Natural Language processing and Machine Learning for In-depth Analysis of Insurance Claims;Network Intrusion Detection with SMOTE-ENN and Deep Learning Techniques;leveraging Transfer Learning to Enhance Location Accuracy in Mapping Services: A Case Study of Google Maps;assessment of Enhanced Email Spam Detection System Through Machine Learning algorithms;machine Learning Methods for Predicting Traffic Congestion Forecasting;hybridization of Computational Intelligence Algorithm for Scheduling of Tasks and Balancing of Load in Cloud Network;MDSV: Mobs Detection by Enhanced Fused Feature Base Deep Neural Network from Surveillance Camera;ioT-Based Solution for Enhanced Tracking of Individuals Living with Dementia;a Novel Task Scheduling Algorithm in Heterogeneous Multi-cloud Environment;Evaluating the Integration and Usage of AI in Higher Education;evaluating the Connectional Benefits of Artificial Intelligence in the Digital Classroom;Influence of AI as an Aspect of Modern Education Era in Present World;Hilbert–Huang Transform Framework-Based Email and SMS Spam Detection;the Advancement and Utilization of Artificial Intelligence and Machine Learning in the Financial Industry and Its Impact on Macro and Microeconomics;analysis on the Cutting-Edge Approach to Assess Artificial Intelligence’s Educational Consequences in Contemporary Studies;data Analytics in Sales and Marketing: A Comprehensive Methodology for Business Analysts;Wireless Energy Transfer for UAV (Drone) Using Machine Learning.

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image Recognition and Enhancement Using Multi-Scale Retinex and Histogram Equalization

Image Recognition and Enhancement Using Multi-Scale Retinex ...

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2023 IEEE International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Senthil Kumar, R. Rajalakhmi, B. Geethika, Palle Indrani, C. Kolalapudi, Ramya Swathi CSE NHCE Bangalore India

ISBN: (纸本)9781665462631

Nowadays, the need for a safe and at ease device is favoured by using every character in society. A fee-effective system is needed for Aerial surveillance systems capable of enhancing situational focus in the course of a couple of scales of place and time. With the assistance of an aerial surveillance machine, we are capable of accumulating a device that could report unmanned vehicles and humans. With improvements in technology, Unmanned motors and optical sensors are being fast produced with powerful usage of resources, therefore, it has ended up being less complicated to get images from the UAV at a low price. But the selection of those pixy received is reduced because of climatic conditions like fog, rain, harsh sunlight, etc. So, we advocate a photograph processing technique as a manner to use algorithms that include single-scale retinex, Multiscale retinex, histogram equalization, and colour recuperation to grow the fine of photographs. These select images have a higher evaluation, and subsequently can be used for better item detection. 1. The program can determine the objects without getting observed.2. The program can find multiple items. Three. MSRP for similar processing like histogram equalization and item detection to get the exceptional possible output © 2023 IEEE.

关键词： image enhancement

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Monitoring Method and Application of Icing Thickness and Icing Type of Distribution Network Overhead Line Based on A Hydrophobic Marker and image Defogging 2

Monitoring Method and Application of Icing Thickness and Ici...

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IEEE 2nd International conference on Power Science and Technology (ICPST)

作者： He, xingping Yang, Jingsi Zeng, Hanye Li, Qingdong Liu, Chengcheng Zhou, Jiye Zhaotong Power Supply Bur Yunnan Power Grid Co Lt China Southern Power Grid Zhaotong Yunnan Peoples R China

ISBN: (纸本)9798350349047;9798350349030

This paper presents a monitoring method and application of icing thickness (ITs) and icing type (ITe) of distribution network overhead line (DNOL) based on a hydrophobic marker (HM) and image defogging (ID). Firstly, a HM tailored specifically for application in DNOL is devised;Subsequently, image processing (IP) techniques are employed to process the acquired images. These techniques include ID, Canny edge detection to identify edges, Imfill Operator for fill holes (or gaps) within binary images, and Bwperim Operator for precise edge extraction, the application of these operator collectively yields a clear and defined edge image. Finally, using the distinct characteristics of various ITe within the RGB color space and utilizing algorithms, the ITe and ITs are accurately calculated. Consequently, relevant information pertaining to line icing of DNOL is obtained, enhancing the accuracy and reliability of line icing monitoring. This, in turn, bolsters the safety and stability of the power system. The results of this study are anticipated to establish a solid theoretical and practical foundation for icing monitoring on DNOL. Furthermore, they are expected to offer substantial support for ensuring the safe and stable operation of power systems, thereby enhancing their overall resilience and reliability.

关键词： distribution network overhead line hydrophobic marker image defogging icing thickness icing type

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Aliasing's Study on Bandpass Sampling

Aliasing's Study on Bandpass Sampling

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2022 systems of Signal Synchronization, Generating and processing in Telecommunications, SYNCHROINFO 2022

作者： Lesnikov, Vladislav Naumovich, Tatiana Chastikov, Alexander Vyatka State University Department of Radio Electronic Systems Kirov Russia

ISBN: (纸本)9781665470643

The increase in the complexity of algorithms for digital processing of RF signals is limited by the need to increase the sampling rate. Sub-Nyquist sampling, in particular bandpass sampling, allows us to reduce the sampling rate, determined by the Nyquist-Shannon sampling theorem, and turn to baseband digital processing. In a large number of publications, the issues of choosing the sampling frequency, which makes it possible to eliminate aliasing, are considered. At the same time, there are works devoted to the restoration of signals distorted by aliasing. Therefore, digital signal processing algorithms are of interest, in which the presence of aliasing is allowed. These algorithms make it possible to further reduce the sampling rate and thus make it easier to process wideband signals or narrowband signals over a wider bandwidth. This paper shows that with a fixed sampling rate and a tunable bandwidth of the processed signals, various configurations of the relative position of the alias of the bandpass signal transferred to the first Nyquist zone and its mirror image are possible. The features of aliasing are considered for various configurations of the down-transferred spectrum and for various orders of aliasing. © 2022 IEEE.

关键词： Digital signal processing

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PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in image-Guided Neurosurgery 36

PhysGNN: A Physics-Driven Graph Neural Network Based Model f...

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36th conference on Neural Information processing systems (NeurIPS)

作者： Salehi, Yasmin Giannacopoulos, Dennis McGill Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9781713871088

Correctly capturing intraoperative brain shift in image-guided neurosurgical procedures is a critical task for aligning preoperative data with intraoperative geometry for ensuring accurate surgical navigation. While the finite element method (FEM) is a proven technique to effectively approximate soft tissue deformation through biomechanical formulations, their degree of success boils down to a trade-off between accuracy and speed. To circumvent this problem, the most recent works in this domain have proposed leveraging data-driven models obtained by training various machine learning algorithms-e.g., random forests, artificial neural networks (ANNs)-with the results of finite element analysis (FEA) to speed up tissue deformation approximations by prediction. These methods, however, do not account for the structure of the finite element (FE) mesh during training that provides information on node connectivities as well as the distance between them, which can aid with approximating tissue deformation based on the proximity of force load points with the rest of the mesh nodes. Therefore, this work proposes a novel framework, PhysGNN, a data-driven model that approximates the solution of the FEM by leveraging graph neural networks (GNNs), which are capable of accounting for the mesh structural information and inductive learning over unstructured grids and complex topological structures. Empirically, we demonstrate that the proposed architecture, PhysGNN, promises accurate and fast soft tissue deformation approximations, and is competitive with the state-of-the-art (SOTA) algorithms while promising enhanced computational feasibility, therefore suitable for neurosurgical settings.

关键词： Finite element method

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A study on Generative Adversarial Text to image Synthesis

A study on Generative Adversarial Text to Image Synthesis

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2023 International conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Tahmid, Marjana Alam, Samiul Akram, Kalim Asif Ashrafi, Kazi Muhammad East Delta University Dept. of Cse Chittagong Bangladesh East Delta University Dept. of Eee Chittagong Bangladesh Ulm University Dept. of Cse Ulm Germany

ISBN: (纸本)9798350322972

image captioning is one of the most prevalent and difficult challenges in Natural Language processing and Computer Vision: given an image, a written description of the image must be developed. The counterpart of the text to image problem is text to image synthesis, which involves creating a picture from a written description. These challenges approximate language translation challenges from a high-level perspective. images and text are two separate "languages"to convey related information, just like equivalent semantics might be represented in two different languages. This is undoubtedly beneficial, however existing AI systems are a long way from achieving this. Strong neural network topologies, such GANs (Generative Adversarial Networks), have been found to provide effective outcomes in recent years. There are, however, some text to image synthesis algorithms that use GAN (Generative Adversarial Network) that attempt to directly map words and characters to image pixels utilizing image synthesis and natural language synthesis approaches. To reconcile these advancements in text as well as image modeling and successfully translate visual representations from characters to pixels, we design an innovative deep architecture and GAN formulation in this study. Here, the demonstration of our model has been done by generating plausible images of birds and flowers from detailed text descriptions. © 2023 IEEE.

关键词： Generative adversarial networks

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