检索结果-内蒙古大学图书馆

A codebook-driven approach for low-light image enhancement

Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Xu Wu Xianxu Hou Zhihui Lai Jie Zhou Ya-nan Zhang Witold Pedrycz Linlin Shen College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou 215400 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060 China School of Computer Science Sichuan Normal University Chengdu 610065 China The Department of Electrical & Computer Engineering University of Alberta Alberta T6G 1H9 Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations; (2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging discrete codebook priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. The code can be obtained from https://***/csxuwu/CodeEnhance or https://***/*** .

关键词：

来源：评论

学校读者我要写书评

暂无评论

Matrix Classifier On Dynamic Functional Connectivity For Mci Identification

Matrix Classifier On Dynamic Functional Connectivity For Mci...

引用

IEEE International Conference on Image Processing

作者： Lei Zhou Liang Zhang Xiao Bai Jun Zhou School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Software Development Environment Beihang University Beijing School of Information and Communication Technology Griffith University Nathan

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

One of the most popular method for Alzheimer's disease (AD) diagnosis is exploring the Brain functional connectivity (FC) from resting-state functional magnetic resonance imaging (RS-fMRI). To early prevent AD, it is crucial to distinguish AD and and its preclinical stage, mild cognitive impairment (MCI) and early MCI (eMCI). In many existing works, dynamic functional connectivity (dFC) which contains rich spatiotemporal information has been exploited for the MCI and eMCI identification. However, most of these dFC based methods only consider the correlation between discrete brain status while ignore the valuable spatiotemporal information contained in dFC. To overcome this limitation, we propose a matrix classifier based method on the dFC signal for MCI and eMCI identification. Specifically, we first represent the dFC correlations by matrix features which contain rich spatiotemporal information and then learn the support matrix machines (SMM) to classify AD and its preclinical stage. Experiments on 600 real people data provide by the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed matrix classifier based method outperforms other FC and dFC based methods for both normal controls (NC)/MCI identification and NC/eMCI identification.

关键词： Feature extraction Correlation Dementia Microsoft Windows Spatiotemporal phenomena Training

来源：评论

学校读者我要写书评

暂无评论

Distributed State Estimation for Heterogeneous Mobile Sensor Networks with Varying Nodes

Distributed State Estimation for Heterogeneous Mobile Sensor...

引用

第三十九届中国控制会议

作者： Yingrong Yu Jie Ren Xiwang Dong Qingdong Li Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing Aerospace System Engineering Research Institute Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

On background of cooperative air combat, state estimation of aerial maneuvering target with multi-sensor cooperative reconnaissance is discussed in this paper. Multiple moving platforms equipped with different kinds of detectors construct heterogeneous mobile sensor networks. Graph theory is adopted to describe the communication topology of the networks. In consideration of detection range limit, the number of sensors participating in the detection changes over time. An interactive multiple model algorithm（IMM） is utilized for target motion modeling. The local observation information is processed by Cubature Information Filter（CIF）. A weighted average consensus-based state estimation method for heterogeneous mobile sensor networks with varying nodes is put forward to fuse information from different sensors and obtain a consistent and precise tracking result. Moreover, the stability analysis of proposed method is provided detailed. Finally, numerical simulation results are given to illustrate the effectiveness of the theoretical results.

关键词： Cubature Information Filter consensus theory distributed state estimation mobile sensor networks stochastic boundedness

来源：评论

学校读者我要写书评

暂无评论

Adaptive Distributed State and Input Estimation Using Retrospective-Cost-Based Information Filter

Adaptive Distributed State and Input Estimation Using Retros...

引用

第三十九届中国控制会议

作者： Hong Wang Liang Han Yuan Liang Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University School of Sino-French Engineer Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

In this paper, the problem of distributed state and input estimation using sensor networks for linear system is investigated. First, the retrospective-cost-based information filter（RCIF） is proposed to estimate the state and input simultaneously, by combining the retrospective cost input estimator subsystem and information filter state estimator subsystem. Next, the retrospective-cost adaptive input estimator subsystem is formulated, which utilizes retrospective cost optimization and recursive minimum mean square estimation to drive the estimated input to approximate the actual input without prior information. Then,the consensus algorithm is used to extend the RCIF to distributed estimation, and to improve the convergence rate, the adaptive update law of consensus weights is presented. Finally, a simulation example is illustrated to validate the effectiveness and feasibility of the proposed algorithm.

关键词： Adaptive Distributed Estimation State estimation Input estimation Retrospective cost optimization Consensus

来源：评论

学校读者我要写书评

暂无评论

StarGAT: Star-Shaped Hierarchical Graph Attentional Network for Heterogeneous Network Representation Learning

StarGAT: Star-Shaped Hierarchical Graph Attentional Network ...

引用

IEEE International Conference on data Mining (ICDM)

作者： Wen-Zhi Li Ling Huang Chang-Dong Wang Yu-Xin Ye School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Computational Science Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China College of Mathematics and Informatics South China Agricultural University Guangzhou China Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application Guangzhou China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781665423991

Many real-world graphs can be viewed as Heterogeneous Networks or Heterogeneous Information Networks (HINs) for that they comprise a diversity of node types and relation types. Due to the efficient representation ability of Graph Neural Network and the idea of random walk, many recent studies apply graph representation learning to HINs and achieve satisfactory results. However, these works either treat different node types in a metapath equally, which is inconsistent with the original graph semantic information for that different node types should have different statuses, or only consider the first-order (node-level) and second-order (metapath-level, a.k.a. link-level) information aggregation while ignoring the higher-order relations. To tackle these two problems, we propose a novel Star-Shaped Hierarchical Graph Attentional Network (StarGAT) model to boost representation learning in HINs. Specifically, we assume nodes in HINs can be categorized into a star-shaped structure including one center node type and a bunch of auxiliary node types in a specific task; and we encode node-level, link-level and motif-level attentions in a hierarchical manner to capture richer semantic information. Extensive experiments on three datasets illustrate the model effectiveness.

关键词： Representation learning Conferences Semantics Heterogeneous networks Graph neural networks data mining Task analysis

来源：评论

学校读者我要写书评

暂无评论

Cross-reference stitching quality assessment for 360◦ omnidirectional images 27

Cross-reference stitching quality assessment for 360◦ omnid...

引用

27th ACM International Conference on Multimedia, MM 2019

作者： Li, Jia Yu, Kaiwen Zhao, Yifan Zhang, Yu Xu, Long State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University China National Astronomical Observatories Chinese Academy of Sciences China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

ISBN: (纸本)9781450368896

Along with the development of virtual reality (VR), omnidirectional images play an important role in producing multimedia content with an immersive experience. However, despite various existing approaches for omnidirectional image stitching, how to quantitatively assess the quality of stitched images is still insufficiently explored. To address this problem, we first establish a novel omnidirectional image dataset containing stitched images as well as dual-fisheye images captured from standard quarters of 0◦, 90◦, 180◦, and 270◦. In this manner, when evaluating the quality of an image stitched from a pair of fisheye images (e.g., 0◦ and 180◦), the other pair of fisheye images (e.g., 90◦ and 270◦) can be used as the cross-reference to provide ground-truth observations of the stitching regions. Based on this dataset, we propose a set of Omnidirectional Stitching Image Quality Assessment (OS-IQA) metrics. In these metrics, the stitching regions are assessed by exploring the local relationships between the stitched image and its cross-reference with histogram statistics, perceptual hash and sparse reconstruction, while the whole stitched images are assessed by the global indicators of color difference and fitness of blind *** and quantitative experiments show our method outperforms the classic IQA metrics and is highly consistent with human subjective evaluations. To the best of our knowledge, it is the first attempt that assesses the stitching quality of omnidirectional images by using cross-references. © 2019 Association for computing Machinery.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

A Distributed Privacy-Preserving Learning Dynamics in General Social Networks

arXiv

引用

arXiv 2020年

作者： Tao, Youming Chen, Shuzhen Li, Feng Yu, Dongxiao Yu, Jiguo Sheng, Hao School of Computer Science and Technology Shandong University Qingdao266237 China Big Data Institute Qilu University of Technology Jinan250353 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

A Combined Guidance Law for Intercepting Hypersonic Large Maneuvering Targets

A Combined Guidance Law for Intercepting Hypersonic Large Ma...

引用

Chinese Automation Congress (CAC)

作者： Bingqian Zhao Xiwang Dong Qingdong Li Zhang Ren Science and Technology on Aircraft Control Laboratory School of Automation Science and Electronic Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Automation Science and Electronic Engineering Beihang University Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper proposes a combined guidance law based on differential geometry and true proportional guidance (TPN). Due to the increase of the interceptor acceleration component perpendicular to the speed direction, the line-of- sight angular rate can be converged quickly, and the convergence rate of the miss distance is faster than that of TPN. Therefore, the combined guidance law can intercept the target in a short time, reducing the possibility of target escape. This paper gives a numerical simulation example under the two conditions of large maneuvering and snake maneuvering of the target, both of which achieve effective interception of the target. Therefore, the designed combined guidance law is effective for intercepting hypersonic large maneuvering targets.

关键词： Angular velocity Geometry Acceleration Mathematical model Aircraft navigation Technological innovation Trajectory

来源：评论

学校读者我要写书评

暂无评论

Time-varying formation tracking control for UAV-UGV heterogeneous swarm systems with switching directed topologies

Time-varying formation tracking control for UAV-UGV heteroge...

引用

International Conference on Control and Automation (ICCA)

作者： Siquan Zhou Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing P.R. China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing P.R. China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper studies time-varying formation tracking control problems for UAV-UGV heterogeneous swarm systems. The UAVs and UGVs need to achieve the desired time-varying formation and track the reference trajectory of a leader synchronously under switching directed topologies. Firstly, the cooperative control model for UAV-UGV heterogeneous swarm system is established by using algebraic graph theory. Then, the formation tracking control protocol based on a distributed observer is designed and the convergence is proved by using piecewise Lyapunov theory. Furthermore, the feasible conditions to achieve the time-varying formation tracking are analyzed, and the algorithm to select the control parameters is proposed. Finally, a numerical simulation example is provided to demonstrate the effectiveness of the proposed control approach.

关键词： Topology Switches Time-varying systems Observers Radar tracking Manganese Control systems

来源：评论

学校读者我要写书评

暂无评论

Finite-Time Time-Varying Group Formation Tracking for High-Order Multi-Agent Systems with Mismatched Disturbances under Directed Graphs

Finite-Time Time-Varying Group Formation Tracking for High-O...

引用

International Conference on Control and Automation (ICCA)

作者： Lei Tian Yongzhao Hua Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing P.R. China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing P.R. China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

Finite-time time-varying group formation (FT-TVGF) tracking control problems for high-order multi-agent systems (MASs) are studied in this paper. Different from previous work, the approaches proposed in this paper combine group formation tracking control with finite-time control, not only that, the agents in each group can achieve time-varying sub-formations and the relative configuration between different groups can be adjusted simultaneously. First, all agents are divided into three types, including the virtual leader, the group leader, and the follower. The virtual leader is added to plan the trajectory of MASs. Subgroups can be interacted with each other by the communication among group leaders. Followers achieve the finite-time formation tracking of group leaders. Then the disturbance observer is constructed to estimate the mismatched disturbances of all group leaders and followers in finite time. Furthermore, based on finite time control theory and observer theory, a distributed control protocol is presented to make MASs realize FT-TVGF tracking under directed graph. The proof of close-loop stability of MASs is accomplished using Lyapunov theory. Finally, numerical simulations verify the theoretical analysis of this paper.

关键词： Disturbance observers Trajectory Tracking Protocols Directed graphs Technological innovation Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：