Information Fusion最新文献_第2页

Multi-View Fusion Graph Attention Network for Multilabel Class Incremental Learning 多标签类增量学习的多视图融合图注意网络

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-22 DOI: 10.1016/j.inffus.2025.103309

Anhui Tan , Yu Wang , Wei-Zhi Wu , Weiping Ding , Jiye Liang

{"title":"Multi-View Fusion Graph Attention Network for Multilabel Class Incremental Learning","authors":"Anhui Tan , Yu Wang , Wei-Zhi Wu , Weiping Ding , Jiye Liang","doi":"10.1016/j.inffus.2025.103309","DOIUrl":"10.1016/j.inffus.2025.103309","url":null,"abstract":"<div><div>Multilabel Class-Incremental Learning (MLCIL) refers to a variant of class-incremental learning and multilabel learning where models are required to learn from images or data associated with multiple labels, and new sets of classes are introduced incrementally. However, most existing MLCIL methods tend to rely heavily on limited single-view features, which makes it challenging for them to effectively capture class-specific characteristics and the correlations between different labels. Furthermore, MLCIL faces difficulties related to both intra-class and inter-class imbalances, which arise from the varying frequencies of class occurrences during each incremental session. To address these issues, we propose a novel MLCIL model called the Multi-View Fusion Graph Attention Network (MVGAT). First, the MVGAT architecture includes a multi-view feature extraction module that fuses class node features from three different perspectives of images, effectively capturing both local and global class-specific information. Second, MVGAT introduces a multi-view attention fusion module that combines the multi-view class node features based on label correlations. Importantly, the attention fusion modules trained in previous learning sessions are preserved, helping to mitigate catastrophic forgetting by providing independent probability predictions for their respective learned classes. Additionally, MVGAT is equipped with a pseudo-label correction module to enhance the accuracy of pseudo-labels by integrating predictions from the current session with those from historical frozen attention fusion modules. Moreover, an asymmetric loss function has been developed to balance intra-class and inter-class performance by dynamically adjusting negative focus parameters based on class occurrence frequency. Finally, experimental results on benchmark datasets demonstrate that MVGAT outperforms existing state-of-the-art methods.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103309"},"PeriodicalIF":14.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Entropy-aware dynamic path selection network for multi-modality medical image fusion 多模态医学图像融合的熵感知动态路径选择网络

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-22 DOI: 10.1016/j.inffus.2025.103312

Jiantao Qu , Dongjin Huang , Yongsheng Shi , Jinhua Liu , Wen Tang

{"title":"Entropy-aware dynamic path selection network for multi-modality medical image fusion","authors":"Jiantao Qu , Dongjin Huang , Yongsheng Shi , Jinhua Liu , Wen Tang","doi":"10.1016/j.inffus.2025.103312","DOIUrl":"10.1016/j.inffus.2025.103312","url":null,"abstract":"<div><div>Deep learning has achieved significant success in multi-modality medical image fusion (MMIF). Nevertheless, the distribution of spatial information varies across regions within a medical image. Current methods consider the medical image as a whole, leading to uneven fusion and susceptibility to artifacts in edge regions. To address this problem,we delve into regional information fusion and introduce an entropy-aware dynamic path selection network (EDPSN). Specifically, we introduce a novel edge enhancement module (EEM) to mitigate artifacts in edge regions through central concentration gradient (CCG). Additionally, an entropy-aware division (ED) module is designed to delineate the spatial information levels of distinct regions in the image through entropy convolution. Finally, a dynamic path selection (DPS) module is introduced to enable adaptive fusion of diverse spatial information regions. Experimental comparisons with some state-of-the-art image fusion methods illustrate the outstanding performance of the EDPSN in three datasets encompassing MRI-CT, MRI-PET, and MRI-SPECT. Moreover, the robustness of the proposed method is validated on the CHAOS dataset, and the clinical value of the proposed method is validated by sixteen doctors and medical students.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103312"},"PeriodicalIF":14.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fusion3M: Community-based multi-scale co-evolving network for dynamic graph representation learning Fusion3M：基于社区的多尺度协同进化网络，用于动态图表示学习

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-22 DOI: 10.1016/j.inffus.2025.103308

Chao Li , Qianyu Song , Runshuo Liu , Zhongying Zhao , Qingtian Zeng

{"title":"Fusion3M: Community-based multi-scale co-evolving network for dynamic graph representation learning","authors":"Chao Li , Qianyu Song , Runshuo Liu , Zhongying Zhao , Qingtian Zeng","doi":"10.1016/j.inffus.2025.103308","DOIUrl":"10.1016/j.inffus.2025.103308","url":null,"abstract":"<div><div>Dynamic Graph Neural Networks have been demonstrated to be effective in modeling dynamic graph structured data, which enables them to solve tasks such as node classification, link prediction, and popular prediction. Existing research has shown a variety of structures within dynamic graphs, ranging from individual representations characterized by microscopic structure to graph representations characterized by macroscopic structure. However, current works primarily focus on the individual level, while neglecting the group states characterized by mesoscopic structure and the intricate correlations between these different levels. In this work, we present ComCo, a <u>Co</u>mmunity-based <u>m</u>ulti-scale <u>Co</u>-evolving network to establish the intrinsic correlations for the Fusion of Microscopic, Mesoscopic and Macroscopic structure information (Fusion3M). Specifically, we extract interactions and encode historical information to model micro-level representations. Additionally, we incorporate a novel community detection mechanism with graph pooling to model meso-level and macro-level representations. Finally, our framework leverages a hierarchical transformer encoder to achieve multi-scale information fusion by learning the complex relationships between different structural scales. Experimental results on seven real-world datasets and nine baselines demonstrate that our model significantly outperforms the current state-of-the-art techniques on multiple downstream tasks. The source code and data have been made available at <span><span>https://github.com/SSQiana/ComCo</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103308"},"PeriodicalIF":14.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MV-BMR: A real-time Motion and Vision Sensing Integration based Agile Badminton Robot MV-BMR：基于实时运动和视觉传感集成的敏捷羽毛球机器人

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-21 DOI: 10.1016/j.inffus.2025.103337

Zhiwei Shi , Xingyu Zhang , Chengxi Zhu , Haochen Wang , Jun Yan , Fan Yang , Dong Xuan

{"title":"MV-BMR: A real-time Motion and Vision Sensing Integration based Agile Badminton Robot","authors":"Zhiwei Shi , Xingyu Zhang , Chengxi Zhu , Haochen Wang , Jun Yan , Fan Yang , Dong Xuan","doi":"10.1016/j.inffus.2025.103337","DOIUrl":"10.1016/j.inffus.2025.103337","url":null,"abstract":"<div><div>This paper presents the Motion and Vision Sensing Integration-based Agile Badminton Robot (MV-BMR), a real-time system that plays badminton with human players. Current badminton robots excel at handling low-speed strikes, such as high clears and net shots, but struggle with high-speed cases, particularly short shots. This challenge arises from two key factors: the shuttlecock’s short flight time, which ranges from 500 to 1000 ms for flat and low flat shots, and the extensive range of the robot’s hit zone. This lingering problem highlights the necessity of designing a dynamic and precise badminton robot. We propose an innovative two-stage approach that incorporates trajectory prediction and control modules to address this challenge. In the first stage, we design the Shuttlecock Early Prediction Network (SEPNet) to estimate the robot’s hit zone with an Inertial Measurement Unit (IMU) mounted on the racket so that the robot can move immediately after a player hits the shuttlecock. In the second stage, we employ a data-driven method, which exploits detected trajectories of shuttlecocks to determine hit points and control the corresponding robot to accurately hit the shuttlecock with Nonlinear Model Predictive Control (NMPC). We have implemented such a real-time system and conducted extensive experiments. The average successful hit rate for short shots of 92.2% and the most extended rallies of 68 demonstrates that our design effectively overcomes the challenges. The video demonstration is available at: <span><span>https://youtu.be/lQo1Ls5Rj3o</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103337"},"PeriodicalIF":14.7,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CGGL: A client-side generative gradient leakage attack with double diffusion prior CGGL：具有双重扩散先验的客户端生成梯度泄漏攻击

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-21 DOI: 10.1016/j.inffus.2025.103292

Bin Pu , Zhizhi Liu , Liwen Wu , Kai Xu , Bocheng Liang , Ziyang He , Benteng Ma , Lei Zhao

{"title":"CGGL: A client-side generative gradient leakage attack with double diffusion prior","authors":"Bin Pu , Zhizhi Liu , Liwen Wu , Kai Xu , Bocheng Liang , Ziyang He , Benteng Ma , Lei Zhao","doi":"10.1016/j.inffus.2025.103292","DOIUrl":"10.1016/j.inffus.2025.103292","url":null,"abstract":"<div><div>Federated learning (FL) has emerged as a widely adopted privacy-preserving distributed framework that facilitates information fusion and model training across multiple clients without requiring direct data sharing with a central server. Despite its advantages, recent studies have revealed that FL is vulnerable to gradient inversion attacks, wherein adversaries can reconstruct clients’ private training data from shared gradients. These existing attacks often assumed typically unrealistic in practical FL deployments. In real-world scenarios, malicious clients are more likely to initiate such attacks. In this paper, we propose a novel <strong><em><u>C</u></em></strong>lient-side <strong><em><u>G</u></em></strong>enerative <strong><em><u>G</u></em></strong>radient <strong><em><u>L</u></em></strong>eakage (<strong>CGGL</strong>) attack tailored for FL-based information fusion scenarios. Our approach targets gradient inversion attacks originating from clients and introduces an adaptive poisoning strategy. By utilizing poisoned gradients in the local updates, a malicious client can stealthily embed the target gradients into the aggregated global model updates, enabling the reconstruction of private data from the aggregated gradients. To enhance the effectiveness of the attack, we further develop a reconstruction framework based on a conditional diffusion model incorporating dual diffusion priors. This design significantly improves image reconstruction fidelity, particularly under larger batch sizes and on high-resolution datasets. We validate the proposed CGGL method through extensive experiments on both natural and medical imaging datasets. Results demonstrate that CGGL consistently outperforms existing client-side gradient inversion attacks, achieving pixel-level data reconstruction and revealing substantial privacy risks in FL-enabled information fusion systems—even in the presence of various defense mechanisms.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103292"},"PeriodicalIF":14.7,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-view information fusion based on federated multi-objective neural architecture search for MRI semantic segmentation 基于联邦多目标神经结构搜索的多视图信息融合MRI语义分割

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-21 DOI: 10.1016/j.inffus.2025.103301

Bin Cao , Huanyu Deng , Yiming Hao , Xiao Luo

{"title":"Multi-view information fusion based on federated multi-objective neural architecture search for MRI semantic segmentation","authors":"Bin Cao , Huanyu Deng , Yiming Hao , Xiao Luo","doi":"10.1016/j.inffus.2025.103301","DOIUrl":"10.1016/j.inffus.2025.103301","url":null,"abstract":"<div><div>With the rapid development of artificial intelligence, medical image semantic segmentation is being used more widely. However, centralized training can lead to privacy risks. At the same time, MRI provides multiple views that together describe the anatomical structure of a lesion, but a single view may not fully capture all features. Therefore, integrating multi-view information in a federated learning setting is a key challenge for improving model generalization. This study combines federated learning, neural architecture search (NAS) and data fusion techniques to address issues related to data privacy, cross-institutional data distribution differences and multi-view information fusion in medical imaging. To achieve this, we propose the FL-MONAS framework, which leverages the advantages of NAS and federated learning. It uses a Pareto-frontier-based multi-objective optimization strategy to effectively combine 2D MRI with 3D anatomical structures, improving model performance while ensuring data privacy. Experimental results show that FL-MONAS maintains strong segmentation performance even in non-IID scenarios, providing an efficient and privacy-friendly solution for medical image analysis.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103301"},"PeriodicalIF":14.7,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable breast cancer diagnosis using histopathology and lesion mask as domain concepts conditional simulation ultrasonography 以组织病理学和病变掩膜为领域概念的条件模拟超声可解释的乳腺癌诊断

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-20 DOI: 10.1016/j.inffus.2025.103343

Guowei Dai , Chaoyu Wang , Qingfeng Tang , Yi Zhang , Duwei Dai , Lang Qiao , Jiaojun Yan , Hu Chen

{"title":"Interpretable breast cancer diagnosis using histopathology and lesion mask as domain concepts conditional simulation ultrasonography","authors":"Guowei Dai , Chaoyu Wang , Qingfeng Tang , Yi Zhang , Duwei Dai , Lang Qiao , Jiaojun Yan , Hu Chen","doi":"10.1016/j.inffus.2025.103343","DOIUrl":"10.1016/j.inffus.2025.103343","url":null,"abstract":"<div><div>Breast cancer diagnosis using ultrasound imaging presents challenges due to inherent limitations in image quality and the complex nature of lesion interpretation. We propose SgmaFuse, a novel interpretable multimodal framework that integrates histopathological concepts and lesion masks information , treated as domain concepts, with ultrasound imaging for accurate and explainable breast cancer diagnosis. At its core, SgmaFuse employs a Spatially Guided Multi-Level Alignment Mechanism (SGMLAM) that orchestrates global–local feature interactions across modalities. This is achieved through a sophisticated hierarchical strategy incorporating cross-modal fusion and attention-based feature correspondence at four distinct levels: global image-report alignment, local mask-guided attention report alignment, local image diagnostic report alignment, and concept-level diagnostic report alignment. Concurrently, a Histological Semantic Activation Vector Learning (HSAVL) module, leveraging kernel Support Vector Machines, learns discriminative semantic concepts directly from histopathological data, thereby bridging the gap between ultrasound imaging features and established pathological patterns via robust concept-level alignment. The framework ability to provide transparent, structured diagnostic explanations through interpretable visual attention maps and semantic concept contributions demonstrates its potential as a reliable clinical decision support tool, particularly in the challenging domain of breast ultrasound diagnosis.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103343"},"PeriodicalIF":14.7,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy processing applied to improve multimodal sensor data fusion to discover frequent behavioral patterns for smart healthcare 应用模糊处理改进多模态传感器数据融合，为智能医疗发现频繁的行为模式

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-20 DOI: 10.1016/j.inffus.2025.103307

Carlos Fernandez-Basso , David Díaz-Jimenez , Jose L. López , Macarena Espinilla

{"title":"Fuzzy processing applied to improve multimodal sensor data fusion to discover frequent behavioral patterns for smart healthcare","authors":"Carlos Fernandez-Basso , David Díaz-Jimenez , Jose L. López , Macarena Espinilla","doi":"10.1016/j.inffus.2025.103307","DOIUrl":"10.1016/j.inffus.2025.103307","url":null,"abstract":"<div><div>The extraction and utilization of latent information from sensor data is gaining increasing prominence due to its potential for transforming decision-making processes across various sectors. Data mining techniques provide robust tools for analyzing large-scale data generated by advanced network management systems, offering actionable insights that drive operational efficiency and strategic improvements. However, the sheer volume of sensor data, combined with challenges related to real-world sensor deployment and user interaction, necessitates the development of advanced data fusion and processing frameworks. This paper presents an innovative automatic fusion and fuzzification methodology designed to integrate multi-source sensor data into coherent, high-quality intelligent outputs. By applying fuzzy logic, the proposed system enhances the interpretability and interoperability of complex sensor datasets. The approach has been validated in a real-world scenario within sensorized homes of Type II diabetic patients in Cabra (Córdoba, Spain), where it aids healthcare professionals in monitoring and optimizing patient routines. Experimental results demonstrate the system’s effectiveness in identifying and analyzing behavioral patterns, highlighting its potential to improve patient care through advanced sensor data fusion techniques.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103307"},"PeriodicalIF":14.7,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-scale dual-attention frequency fusion for joint segmentation and deformable medical image registration 多尺度双关注频率融合关节分割与形变医学图像配准

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-20 DOI: 10.1016/j.inffus.2025.103293

Hongchao Zhou , Shiyu Liu , Shunbo Hu

{"title":"Multi-scale dual-attention frequency fusion for joint segmentation and deformable medical image registration","authors":"Hongchao Zhou , Shiyu Liu , Shunbo Hu","doi":"10.1016/j.inffus.2025.103293","DOIUrl":"10.1016/j.inffus.2025.103293","url":null,"abstract":"<div><div>Deformable medical image registration is a crucial aspect of medical image analysis. Improving the accuracy and plausibility of registration by information fusion is still a problem that needs to be addressed. To solve this problem, we propose DAFF-Net, a novel framework that systematically unifies three kind of information fusion (low-level fusion, high-level fusion, and loss fusion) to enhance registration precision and plausibility: (i) low-level fusion: DAFF-Net employs a shared global encoder to extract common anatomical features from both moving and fixed images in two tasks, reducing redundancy and ensuring foundational consistency across tasks; (ii) high-level fusion: through the dual attention frequency fusion (DAFF) module, DAFF-Net dynamically combines multi-scale registration and segmentation features, leverages features of low-frequency structural coherences and high-frequency boundary details, and adaptively reweighting them to enhance registration via global and local attention mechanisms; (iii) loss fusion: a unified loss function enforces bidirectional consistency, i.e., segmentation supervises registration through anatomical constraints, while registration refines segmentation via deformation-correct anatomical consistency. Extensive experiments on three public 3D brain magnetic resonance imaging (MRI) datasets demonstrate that the proposed DAFF-Net and its unsupervised variant outperform state-of-the-art registration methods across several evaluation metrics, demonstrating the effectiveness of our approach in deformable medical image registration. The proposed framework holds promise for practical clinical applications such as preoperative planning, longitudinal disease tracking, and structural analysis in neurological disorders.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103293"},"PeriodicalIF":14.7,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hyperspectral super-resolution via nonlinear unmixing 通过非线性解混的高光谱超分辨率

IF 14.7 1区计算机科学

Information Fusion Pub Date : 2025-05-20 DOI: 10.1016/j.inffus.2025.103295

Qingke Zou , Jie Zhou , Mingjie Luo

{"title":"Hyperspectral super-resolution via nonlinear unmixing","authors":"Qingke Zou , Jie Zhou , Mingjie Luo","doi":"10.1016/j.inffus.2025.103295","DOIUrl":"10.1016/j.inffus.2025.103295","url":null,"abstract":"<div><div>Fusing a hyperspectral image (HSI) with a multispectral image (MSI) to produce a super-resolution image (SRI) that possesses both fine spatial and spectral resolutions is a widely adopted technique in hyperspectral super-resolution (HSR). Most existing HSR methods accomplish this task within the framework of linear mixing model (LMM). However, a severe challenge lies in the inherent linear constraint of LMM, which hinders the adaptability of these HSR methods to complex real-world scenarios. In this work, the LMM is extended to the generalized bilinear model (GBM), and a novel HSR method based on nonnegative tensor factorization is proposed in the framework of nonlinear unmixing. Apart from the linear part, it additionally considers the main nonlinear interactions, that is, the bilinear interactions between the endmembers. Crucially, each potential decomposition factor possesses a physical interpretation, enabling the incorporation of prior information to enhance reconstruction performance. Furthermore, an HSR algorithm has been devised specifically for scenarios where the spatial degradation operators from SRI to HSI are unknown, which undoubtedly enhances its practical applicability. The proposed methods overcome the inherent linear limitations of the LMM framework while avoiding the information loss associated with matrixizing HSI and MSI. The effectiveness of the proposed methods is showcased through simulated and real data.</div></div>","PeriodicalId":50367,"journal":{"name":"Information Fusion","volume":"123 ","pages":"Article 103295"},"PeriodicalIF":14.7,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0