IEEE Signal Processing Letters最新文献_第10页

Robust Multitask Diffusion Bias Compensation M-Estimate Algorithms for Distributed Adaptive Learning With Noisy Input 带有噪声输入的分布式自适应学习鲁棒扩散偏差补偿m -估计算法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-04 DOI: 10.1109/LSP.2025.3547668

Senran Peng;Lijuan Jia;Zi-Jiang Yang;Ran Tao;Yue Wang

引用次数: 0

Bandpass Filters: A Penalized Least-Squares Optimization With $boldsymbol ell _{1}$-Norm Regularization Design 带通滤波器：基于范数正则化设计的惩罚最小二乘优化

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-04 DOI: 10.1109/LSP.2025.3547884

Arman Kheirati Roonizi;Roberto Sassi

引用次数: 0

FastPSA: A Fast Version of the Principal Skewness Analysis FastPSA：主偏度分析的快速版本

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3546893

Jingyu Gao;Xiurui Geng;Luyan Ji

{"title":"FastPSA: A Fast Version of the Principal Skewness Analysis","authors":"Jingyu Gao;Xiurui Geng;Luyan Ji","doi":"10.1109/LSP.2025.3546893","DOIUrl":"https://doi.org/10.1109/LSP.2025.3546893","url":null,"abstract":"Recently, principal skewness analysis (PSA) has been introduced into the domain of feature extraction. It is equivalent to the skewness version of Fast independent component analysis (FastICA). Unlike FastICA, PSA does not require all sample points when searching for the projection directions, making it faster. However, for the data of dimension <inline-formula><tex-math>$L$</tex-math></inline-formula>, PSA needs to calculate the eigenvectors of <inline-formula><tex-math>$L$</tex-math></inline-formula> tensors, each of size <inline-formula><tex-math>$Ltimes Ltimes L$</tex-math></inline-formula>. When <inline-formula><tex-math>$L$</tex-math></inline-formula> is large, PSA still requires significant computational time to find all projection directions. In this letter, we find that the <inline-formula><tex-math>$(m+1)$</tex-math></inline-formula>th projection direction in PSA can be obtained by calculating the eigenvector of a tensor of size <inline-formula><tex-math>$(L-m)times (L-m)times (L-m)$</tex-math></inline-formula>. Furthermore, we propose a fast version of PSA (FastPSA) that is mathematically equivalent to PSA. The experimental results demonstrate that FastPSA has lower computational complexity than PSA.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1226-1230"},"PeriodicalIF":3.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Copy-Move Detection in Optical Microscopy: A Segmentation Network and a Dataset 光学显微镜中的复制移动检测：分割网络和数据集

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3547273

Hao-Chiang Shao;Yuan-Rong Liao;Tse-Yu Tseng;Yen-Liang Chuo;Fong-Yi Lin

{"title":"Copy-Move Detection in Optical Microscopy: A Segmentation Network and a Dataset","authors":"Hao-Chiang Shao;Yuan-Rong Liao;Tse-Yu Tseng;Yen-Liang Chuo;Fong-Yi Lin","doi":"10.1109/LSP.2025.3547273","DOIUrl":"https://doi.org/10.1109/LSP.2025.3547273","url":null,"abstract":"With increasing revelations of academic fraud, detecting forged experimental images in the biomedical field has become a public concern. The challenge lies in the fact that copy-move targets can include background tissue, small foreground objects, or both, which may be out of the training domain and subject to unseen attacks, rendering standard object-detection-based approaches less effective. To address this, we reformulate the problem of detecting biomedical copy-move forgery regions as an intra-image co-saliency detection task and propose CMSeg-Net, a copy-move forgery segmentation network capable of identifying unseen duplicated areas. Built on a multi-resolution encoder-decoder architecture, CMSeg-Net incorporates self-correlation and correlation-assisted spatial-attention modules to detect intra-image regional similarities within feature tensors at each observation scale. This design helps distinguish even small copy-move targets in complex microscopic images from other similar objects. Furthermore, we created a copy-move forgery dataset of optical microscopic images, named FakeParaEgg, using open data from the ICIP 2022 Challenge to support CMSeg-Net's development and verify its performance. Extensive experiments demonstrate that our approach outperforms previous state-of-the-art methods on the FakeParaEgg dataset and other open copy-move detection datasets, including <bold>CASIA-CMFD</b>, <bold>CoMoFoD</b>, and <bold>CMF</b>.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1106-1110"},"PeriodicalIF":3.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HashShield: A Robust DeepFake Forensic Framework With Separable Perceptual Hashing HashShield：一个具有可分离感知哈希的鲁棒深度伪造取证框架

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3547664

Meihong Yang;Baolin Qi;Ruihe Ma;Yongjin Xian;Bin Ma

引用次数: 0

Embedding Blake–Zisserman Regularization in Unfolded Proximal Neural Networks for Enhanced Edge Detection 在未折叠近端神经网络中嵌入Blake-Zisserman正则化以增强边缘检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3547671

Hoang Trieu Vy Le;Marion Foare;Audrey Repetti;Nelly Pustelnik

引用次数: 0

Improving the Second Association for Multi-Object Tracking by Directed Reliable Neighbor Graphs 用有向可靠邻居图改进多目标跟踪的二次关联

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3546896

Yubo Zhang;Liying Zheng;Qingming Huang

{"title":"Improving the Second Association for Multi-Object Tracking by Directed Reliable Neighbor Graphs","authors":"Yubo Zhang;Liying Zheng;Qingming Huang","doi":"10.1109/LSP.2025.3546896","DOIUrl":"https://doi.org/10.1109/LSP.2025.3546896","url":null,"abstract":"The second association enhances Multi-Object Tracking (MOT) by reducing missed detections and trajectory fragmentation but is limited by the poor distinguishability of low-confidence detections. To address this, we introduce interactive features leveraging Graph Neural Networks (GNNs) to enhance object distinction. Unlike existing GNN-based trackers that compute interactive features for all objects, we selectively calculate interactive features based on directed reliable neighbor graphs for objects in the second stage. These graphs include two kinds of nodes: reliable nodes (already associated in the first association) and unreliable nodes (remaining objects). Bidirectional edges between reliable nodes indicate matches, while directed edges from reliable to unreliable nodes represent neighbor relationships. These graphs are forwarded to graph attention networks to obtain interactive features combined with motion features for the second association. Experimental results on the MOT17 (65.3 in HOTA, 80.6 in IDF1) and MOT20 (63.8 in HOTA, 78.0 in IDF1) benchmark datasets demonstrate the effectiveness of our proposed tracker, particularly in HOTA and IDF1.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1151-1155"},"PeriodicalIF":3.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint TR Beam Selection and Power Allocation Strategy for MTT in Netted Radar Systems 网络化雷达系统中MTT联合TR波束选择与功率分配策略

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3546886

Jingjing Guo;Haihong Tao

引用次数: 0

MIMO Radar Joint Heart Rate and Respiratory Rate Estimation and Performance Bound Analysis MIMO雷达联合心率和呼吸频率估计及性能界分析

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3546894

Peichao Wang;Qian He;Haozheng Li

引用次数: 0

GLNet: Global-Local Fusion Network for Strip Steel Surface Defects Detection 基于全局-局部融合网络的带钢表面缺陷检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-03 DOI: 10.1109/LSP.2025.3546888

Xuan Wang;Liuxin Bao;Xiaofei Zhou;Lei Xia;Xiaobin Xu

{"title":"GLNet: Global-Local Fusion Network for Strip Steel Surface Defects Detection","authors":"Xuan Wang;Liuxin Bao;Xiaofei Zhou;Lei Xia;Xiaobin Xu","doi":"10.1109/LSP.2025.3546888","DOIUrl":"https://doi.org/10.1109/LSP.2025.3546888","url":null,"abstract":"Surface defect detection in strip steel is a critical task in industrial quality control. However, existing methods struggle with capturing both local details and global context effectively. In this paper, we propose the Global-Local Fusion Network (GLNet) for strip steel surface defect detection, which combines the advantages of VMamba's global feature extraction and CNN's local feature modeling. GLNet employs an encoder-decoder structure, where the encoder consists of two parallel branches: one based on VMamba for capturing global features and the other using ResNet50 for extracting local features. In the decoder, a Global-Local Fusion (GLF) module integrates these features using the Cross Prototype Objective Enhancement (CPOE) and Selective Spatial and Channel Attention (SSCA) modules. The CPOE module facilitates the interaction and fusion between global and local features, while the SSCA module digs the multi-scale information from the global feature through dynamic attention to guide the feature aggregation. Extensive experiments on the ESDIs dataset, demonstrate that GLNet achieves state-of-the-art performance in defect detection, surpassing 13 existing methods in both quantitative and qualitative metrics.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1256-1260"},"PeriodicalIF":3.2,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0