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2nd International conference on Advanced Intelligent Technologies (ICAIT)

作者： Li, Shuying Liu, Wei Yunnan Normal Univ Sch Phys & Elect Informat Kunming Yunnan Peoples R China

ISBN: (纸本)9789811697357;9789811697340

In the process of image processing, image acquisition, acquisition and transmission, the image is usually affected by noise, resulting in "distortion" to varying degrees. image denoising is a very important graphics processing method, which can effectively improve the quality of distributed images and solve the problem of image quality degradation after noise pollution in reality. This article introduces spatial denoising methods, including mean filter, Wiener filter and median filter, and analyzes these methods. By using MATLAB software, the method of image denoising in the spatial domain is studied and tested, and these results are analyzed.

关键词： image denoising MATLAB Mean filtering Median filtering Space domain

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F2RNET: A FULL-RESOLUTION REPRESENTATION NETWORK FOR BIOMEDICAL image SEGMENTATION 29

F2RNET: A FULL-RESOLUTION REPRESENTATION NETWORK FOR BIOMEDI...

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IEEE International conference on image processing (ICIP)

作者： Cheng, Junlong Gao, Chengrui Li, Changlin Ming, Zhangqiang Yang, Yong Wang, Fengjie Zhu, Min Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In this paper, we are interested in exploring the problem of full-resolution image segmentation, with the focus placed on learning full-resolution representations for biomedicine images. We divide the original resolution image into patches of different sizes in different stages and then extracte local features from large to small patches using efficient and flexible components in modern convolutional neural networks (CNN). Meanwhile, a multilayer perceptron (MLP) block intended for modeling long-range dependencies between patches is designed to compensate for the inherent inductive bias caused by convolution operations. In addition, we perform multiscale fusion and receive representation information from parallel paths at each stage, resulting in a rich full-resolution representation. We evaluate the proposed method on different biomedical image segmentation tasks and it achieves a competitive performance compared to the latest deep learning segmentation methods. It is hoped that this method will serve as a useful alternative to biomedical image segmentation and provide an improved idea for the research based on full-resolution representation.

关键词： Full-resolution representation Local feature Multi-scale fusion Biomedical image segmentation

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Towards a Scalable parallel Infomap Algorithm for Community Detection

Towards a Scalable Parallel Infomap Algorithm for Community ...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Gabriel G. Santos Kartik Lakhotia César A. F. De Rose School of Technology PUCRS Porto Alegre Brazil Intel Labs Santa Clara USA

Identifying Community structures is a fundamental problem in graph analysis. To detect communities in massive contemporary graphs, researchers have extensively explored shared- and distributed-memory parallel algorithms for several methods including Louvain Modularity Optimization and Label Propagation. The widely used Infomap algorithm based on Map Equation Framework (MEF) is known to provide better quality results than other approaches. However, research on parallel community detection using MEF or Infomap is extremely sparse when compared to other methods. We present a comprehensive characterization of Infomap and some of its known parallel implementations to facilitate research into parallel algorithms based on MEF. Most implementations take simple parallelization approaches, leaving strategies used to parallelize similar algorithms such as Louvain untouched. We highlight the scalability limitations of current implementations and implement and eval-uate optimizations for MEF based parallel community detection that achieved up to 119% improvement on the overall speedup across the tested datasets.

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A small-sample fault diagnosis approach for RV reducer with RP-SST parallel CNN-SVM model 5

A small-sample fault diagnosis approach for RV reducer with ...

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5th International conference on Mechanical Engineering and Materials, ICMEM 2024

作者： Pan, Jiahao Liu, Chuanmeng Jin, Nan Peng, Yulong Qiu, Guangqi School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology 1958 Hakka Avenue Jiangxi Ganzhou China

ISBN: (数字)9781510688971

ISBN: (纸本)9781510688964

For addressing the problem of low accuracy in the available diagnostic methods due to the insufficient fault-sample, this article proposes a small-sample fault diagnosis method with Recurrence plot (RP), Synchrosqueezed transform (SST) and parallel convolutional neural network (CNN) and support vector machine (SVM). This method first utilizes RP and SST techniques to extract the multimodal features into image processing. Then, the parallel CNN is used for multimodal feature fusion, enabling it to utilize more fault information within a limited number of samples. Finally, SVM is used for fault classification. The fault experiments for RV reducer were performed to verify the effectiveness of the proposed fault diagnosis method. The complementarity of RP-SST in multimodal feature extraction was verified for solving the problem of insufficient feature extraction due to sample size limitations effectively. Further, the SOTA fault diagnosis model showed that the proposed method achieved a fault diagnosis accuracy of 96.26% in small sample situations, significantly better than CNN-BIGRU, CNN-LSTM, and Resnet model. © 2025 SPIE.

关键词： Convolutional neural networks

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Restoration Method of Motion Blurred image Based on Feature Fusion and Particle Swarm Optimization Algorithm 2

Restoration Method of Motion Blurred Image Based on Feature ...

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2nd IEEE International conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Liu, Feng Xinjiang University Physical Education Research Department Urumqi China

ISBN: (纸本)9798350347456

The current computer situation is that people generally have home computers. With the rise and prosperity of the Internet era such as short video, people have higher and higher requirements for high-definition status of pictures and videos. In addition, some people hope to use Internet technology to repair and clarify ancient photos and sports videos. Therefore, in order to make the motion blurred image clearer, this paper studies the image restoration method with the help of feature fusion and particle swarm optimization algorithm. In this paper, we mainly use analytic hierarchy process (AHP) and experimental case analysis and comparison to restore blurred images in different motion states. The experimental results show that the maximum time difference of the proposed method for processing motion blurred images can be 13.1s, which is much smaller than the other two methods. © 2023 IEEE.

关键词： Analytic hierarchy process

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Diffusion Priors for Variational Likelihood Estimation and image Denoising 38

Diffusion Priors for Variational Likelihood Estimation and I...

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38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Cheng, Jun Tan, Shan School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Real-world noise removal is crucial in low-level computer vision. Due to the remarkable generation capabilities of diffusion models, recent attention has shifted towards leveraging diffusion priors for image restoration tasks. However, existing diffusion priors-based methods either consider simple noise types or rely on approximate posterior estimation, limiting their effectiveness in addressing structured and signal-dependent noise commonly found in real-world images. In this paper, we build upon diffusion priors and propose adaptive likelihood estimation and MAP inference during the reverse diffusion process to tackle real-world noise. We introduce an independent, non-identically distributed likelihood combined with the noise precision (inverse variance) prior and dynamically infer the precision posterior using variational Bayes during the generation process. Meanwhile, we rectify the estimated noise variance through local Gaussian convolution. The final denoised image is obtained by propagating intermediate MAP solutions that balance the updated likelihood and diffusion prior. Additionally, we explore the local diffusion prior inherent in low-resolution diffusion models, enabling direct handling of high-resolution noisy images. Extensive experiments and analyses on diverse real-world datasets demonstrate the effectiveness of our method. Code is available at https://***/HUST-Tan/DiffusionVI. © 2024 Neural information processing systems foundation. All rights reserved.

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An image retrieval approach based on feature extraction and self-supervised learning 2

An image retrieval approach based on feature extraction and ...

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2nd International conference on distributed Computing and High Performance Computing (DCHPC)

作者： Kolahkaj, Maral Islamic Azad Univ Sousangerd Branch Dept Comp Engn Sousangerd Iran

ISBN: (纸本)9781665496728

Today, due to the development of technology and the advent of web 2.0 applications, different users prefer to do many of their personal tasks over the Internet. Due to the huge amount of information on the web, retrieving the appropriate information for each user has become a challenging task. Content-based image retrieval is one of the most important research fields in digital image processing domain, which searches the similar images to the target image by extracting visual content from the query image. In this regard, many studies have been conducted to increase the accuracy of image retrieval systems. However, due to the explosive growth of storage resources and the lack of a responsible system for image retrieval, it is still considered as one of the most attractive fields of research. In this paper, a method is proposed that extracts the appropriate features using a hybrid method, and then searches the images that are similar to the target image. In this way, self-supervised learning approach is utilized to provide the most similar images. Experimental results based on the Corel dataset show that the accuracy of the proposed method has increased compared to the other methods.

关键词： image Retrieval feature extraction self-supervised learning frequent pattern mining

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LOFS: A Lightweight Online File Storage Strategy for Effective Data Deduplication at Network Edge

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2022年第10期33卷 2263-2276页

作者： Cheng, Geyao Guo, Deke Luo, Lailong Xia, Junxu Gu, Siyuan Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

Edge computing responds to users' requests with low latency by storing the relevant files at the network edge. Various data deduplication technologies are currently employed at edge to eliminate redundant data chunks for space saving. However, the lookup for the global huge-volume fingerprint indexes imposed by detecting redundancies can significantly degrade the data processing performance. Besides, we envision a novel file storage strategy that realizes the following rationales simultaneously: 1) space efficiency, 2) access efficiency, and 3) load balance, while the existing methods fail to achieve them at one shot. To this end, we report LOFS, a Lightweight Online File Storage strategy, which aims at eliminating redundancies through maximizing the probability of successful data deduplication, while realizing the three design rationales simultaneously. LOFS leverages a lightweight three-layer hash mapping scheme to solve this problem with constant-time complexity. To be specific, LOFS employs the Bloom filter to generate a sketch for each file, and thereafter feeds the sketches to the Locality Sensitivity hash (LSH) such that similar files are likely to be projected nearby in LSH tablespace. At last, LOFS assigns the files to real-world edge servers with the joint consideration of the LSH load distribution and the edge server capacity. Trace-driven experiments show that LOFS closely tracks the global deduplication ratio and generates a relatively low load std compared with the comparison methods.

关键词： Servers Indexes Costs Redundancy Throughput image edge detection Resource management Data deduplication locality sensitivity hash edge computing storage systems

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Fast Commitment for Geo-distributed Transactions via Decentralized Co-coordinators 50th

Fast Commitment for Geo-Distributed Transactions via Decentr...

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50th International conference on Very Large Data Bases, VLDB 2024

作者： Zhang, Zihao Hu, Huiqi Zhou, Xuan Tu, Yaofeng Qian, Weining Zhou, Aoying East China Normal University China ZTE Corporation China

In a geo-distributed database, data shards and their respective replicas are deployed in distinct datacenters across multiple regions, enabling regional-level disaster recovery and the ability to serve globalusers locally. However, transaction processing in geo-distributed databases requires multiple cross-region communications, especially during the commit phase, which can significantly impact system performance. To optimize the performance of geo-distributed transactions, we propose Decentralized Two-phase Commit (D2PC), a new trans action commit protocol aiming to minimize the negative impact of cross-region communication. In D2PC, we employ multiple co coordinators that perform commit coordination in parallel. Each co-coordinator is responsible for collecting 2PC votes and making a PreCommit decision in its local region. This approach allows for the concurrent invocation of multiple cross-region network round trips, and each region can end its concurrency control locally before replication is complete, thus significantly reducing the chances of blocking and enhancing system concurrency. Moreover, we propose the bypass leader replication reply method, leveraging decentralized co-coordinators to bypass the leader for message transmission, thereby reducing the commit latency. Experimental results have demonstrated that D2PC can reduce commit latency by 43% and improve throughput by up to 2.43 × compared to the geo-distributed transaction processing methods based on 2PC © 2024, VLDB Endowment. All rights reserved.

关键词： Concurrency control

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A Review of Evolutionary Optimization methods for Information Visualization and Feature Space Exploration 18

A Review of Evolutionary Optimization Methods for Informatio...

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18th International conference on Innovations in Intelligent Systems and Applications (INISTA)

作者： Gavrilescu, Marius Leon, Florin Ferariu, Lavinia-Eugenia Butincu, Cristian-Nicolae Gheorghe Asachi Tech Univ Iasi Fac Automat Control & Comp Engn Iasi Romania

ISBN: (纸本)9798350368130

Evolutionary optimization plays an important role in the representation of information from data sets originating from various technical fields and natural sciences, as it helps to explore parameter spaces for meaningful representations. Quality-Diversity (QD) methods, notably MAP-Elites variants, prove effective in diverse fields, emphasizing their ability to provide sets of high-performing solutions. This review discusses challenges in single- and multi-objective optimization, with applications in multiple directions such as image processing, visualization, and medical imaging. It also reviews QD algorithms and highlights advancements in algorithmic adaptations, user-driven optimization and the potential to explore complex feature spaces. The presented works contribute to understanding and applying evolutionary optimization for solving visualization and feature space exploration problems in various domains.

关键词： evolutionary optimization quality-diversity MAP-Elites information visualization feature space

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