IEEE Signal Processing Letters最新文献_第3页

Efficient RGBT Tracking via Multi-Path Mamba Fusion Network 基于多路径曼巴融合网络的高效rbt跟踪

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3563123

Fanghua Hong;Wanyu Wang;Andong Lu;Lei Liu;Qunjing Wang

{"title":"Efficient RGBT Tracking via Multi-Path Mamba Fusion Network","authors":"Fanghua Hong;Wanyu Wang;Andong Lu;Lei Liu;Qunjing Wang","doi":"10.1109/LSP.2025.3563123","DOIUrl":"https://doi.org/10.1109/LSP.2025.3563123","url":null,"abstract":"RGBT tracking aims to fully exploit the complementary advantages of visible and infrared modalities to achieve robust tracking, thus the design of multimodal fusion network is crucial. However, existing methods typically adopt CNNs or Transformer networks to construct the fusion network, which poses a challenge in achieving a balance between performance and efficiency. To overcome this issue, we introduce an innovative visual state space (VSS) model, represented by Mamba, for RGBT tracking. In particular, we design a novel multi-path Mamba fusion network that achieves robust multimodal fusion capability while maintaining a linear overhead. First, we design a multi-path Mamba layer to sufficiently fuse two modalities in both global and local perspectives. Second, to alleviate the issue of inadequate VSS modeling in the channel dimension, we introduce a simple yet effective channel swapping layer. Extensive experiments conducted on four public RGBT tracking datasets demonstrate that our method surpasses existing state-of-the-art trackers. Notably, our fusion method achieves higher tracking performance compared to the well-known Transformer-based fusion approach (TBSI), while also achieving 92.8% and 80.5% reductions in parameter count and computational cost, respectively.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1790-1794"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924953","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

TF-CorrNet: Leveraging Spatial Correlation for Continuous Speech Separation tf - cornet：利用空间相关进行连续语音分离

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562819

Ui-Hyeop Shin;Bon Hyeok Ku;Hyung-Min Park

{"title":"TF-CorrNet: Leveraging Spatial Correlation for Continuous Speech Separation","authors":"Ui-Hyeop Shin;Bon Hyeok Ku;Hyung-Min Park","doi":"10.1109/LSP.2025.3562819","DOIUrl":"https://doi.org/10.1109/LSP.2025.3562819","url":null,"abstract":"In general, multi-channel source separation has utilized inter-microphone phase differences (IPDs) concatenated with magnitude information in time-frequency domain, or real and imaginary components stacked along the channel axis. However, the spatial information of a sound source is fundamentally contained in the “differences” between microphones, specifically in the correlation between them, while the power of each microphone also provides valuable information about the source spectrum, which is why the magnitude is also included. Therefore, we propose a network that directly leverages a correlation input with phase transform (PHAT)-<inline-formula><tex-math>$beta$</tex-math></inline-formula> to estimate the separation filter. In addition, the proposed TF-CorrNet processes the features alternately across time and frequency axes as a dual-path strategy in terms of spatial information. Furthermore, we add a spectral module to model source-related direct time-frequency patterns for improved speech separation. Experimental results demonstrate that the proposed TF-CorrNet effectively separates the speech sounds, showing high performance with a low computational cost in the LibriCSS dataset.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1875-1879"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929827","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

Performance Prediction of Hybrid Integration Detector for Radar Moderately Fluctuating Rayleigh Targets 雷达中波动瑞利目标混合积分探测器性能预测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562829

Hongying Zheng;Qilei Zhang;Yongsheng Zhang

引用次数: 0

Decentralized Smoothing ADMM for Quantile Regression With Non-Convex Sparse Penalties 非凸稀疏惩罚分位数回归的分散平滑ADMM

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562828

Reza Mirzaeifard;Diyako Ghaderyan;Stefan Werner

{"title":"Decentralized Smoothing ADMM for Quantile Regression With Non-Convex Sparse Penalties","authors":"Reza Mirzaeifard;Diyako Ghaderyan;Stefan Werner","doi":"10.1109/LSP.2025.3562828","DOIUrl":"https://doi.org/10.1109/LSP.2025.3562828","url":null,"abstract":"In the rapidly evolving internet-of-things (IoT) ecosystem, effective data analysis techniques are crucial for handling distributed data generated by sensors. Addressing the limitations of existing methods, such as the sub-gradient approach, which fails to distinguish between active and non-active coefficients effectively, this paper introduces the decentralized smoothing alternating direction method of multipliers (DSAD) for penalized quantile regression. Our method leverages non-convex sparse penalties like the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD), improving the identification and retention of significant predictors. DSAD incorporates a total variation norm within a smoothing ADMM framework, achieving consensus among distributed nodes and ensuring uniform model performance across disparate data sources. This approach overcomes traditional convergence challenges associated with non-convex penalties in decentralized settings. We present convergence proof and extensive simulation results to validate the effectiveness of the DSAD, demonstrating its superiority in achieving reliable convergence and enhancing estimation accuracy compared with prior methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1915-1919"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937923","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

LYT-NET: Lightweight YUV Transformer-Based Network for Low-Light Image Enhancement LYT-NET：用于弱光图像增强的轻量级YUV变压器网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3563125

Alexandru Brateanu;Raul Balmez;Adrian Avram;Ciprian Orhei;Cosmin Ancuti

引用次数: 0

An Effective Yet Fast Early Stopping Metric for Deep Image Prior in Image Denoising 深度图像去噪中一种有效且快速的早期停止度量

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562948

Xiaohui Cheng;Shaoping Xu;Wuyong Tao

引用次数: 0

SELL:A Method for Low-Light Image Enhancement by Predicting Semantic Priors 基于语义先验预测的弱光图像增强方法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562822

Quanquan Xiao;Haiyan Jin;Haonan Su;Ruixia Yan

引用次数: 0

Mask-Guided Cross-Modality Fusion Network for Visible-Infrared Vehicle Detection 基于掩模制导的可见光-红外车辆检测交叉模态融合网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562816

Lingyun Tian;Qiang Shen;Zilong Deng;Yang Gao;Simiao Wang

{"title":"Mask-Guided Cross-Modality Fusion Network for Visible-Infrared Vehicle Detection","authors":"Lingyun Tian;Qiang Shen;Zilong Deng;Yang Gao;Simiao Wang","doi":"10.1109/LSP.2025.3562816","DOIUrl":"https://doi.org/10.1109/LSP.2025.3562816","url":null,"abstract":"Drone-based vehicle detection is crucial for intelligent traffic management. However, current methods relying solely on single visible or infrared modalities struggle with precision and robustness, especially in adverse weather conditions. The effective integration of cross-modal information to enhance vehicle detection still poses significant challenges. In this letter, we propose a masked-guided cross-modality fusion method, called MCMF, for robust and accurate visible-infrared vehicle detection. Firstly, we construct a framework consisting of three branches, with two dedicated to the visible and infrared modalities respectively, and another tailored for the fused multi-modal. Secondly, we introduce a Location-Sensitive Masked AutoEncoder (LMAE) for intermediate-level feature fusion. Specifically, our LMAE utilizes masks to cover intermediate-level features of one modality based on the prediction hierarchy of another modality, and then distills cross-modality guidance information through regularization constraints. This strategy, through a self-learning paradigm, effectively preserves the useful information from both modalities while eliminating redundant information from each. Finally, the fused features are input into an uncertainty-based detection head to generate predictions for bounding boxes of vehicles. When evaluated on the DroneVehicle dataset, our MCIF reaches 71.42% w.r..t. mAP, outperforming an established baseline method by 7.42%. Ablation studies further demonstrate the effectiveness of our LMAE for visible-infrared fusion.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1815-1819"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925250","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

Boosting of Mutual-Structure Denoising: A Plug-and-Play Solution for Compressive Sampling MRI Reconstruction With Theoretical Guarantees 增强互结构去噪：具有理论保证的压缩采样MRI重构即插即用解决方案

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562820

Qingyong Zhu;Majun Shi;Zhuo-Xu Cui;Hongwu Zeng;Dong Liang

{"title":"Boosting of Mutual-Structure Denoising: A Plug-and-Play Solution for Compressive Sampling MRI Reconstruction With Theoretical Guarantees","authors":"Qingyong Zhu;Majun Shi;Zhuo-Xu Cui;Hongwu Zeng;Dong Liang","doi":"10.1109/LSP.2025.3562820","DOIUrl":"https://doi.org/10.1109/LSP.2025.3562820","url":null,"abstract":"The field of accelerated magnetic resonance imaging (AMRI) has garnered significant attention, focusing on reconstructing target image from compressively sampled k-space data to address an ill-posed linear inverse problem. In this study, we exploit the multiparameterization of MRI to propose a new plug-and-play prior (P<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>) for enhancing reconstruction quality. We begin by introducing a mutual-structure guided P<inline-formula><tex-math>$^{3}$</tex-math></inline-formula> (MS-GP<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>) framework, based on jointly penalized least-squares regression (JPLSR), to selectively transfer common priors from a reference image to the target one, thereby minimizing errors caused by indiscriminate structural replication. Furthermore, we establish a self-sharpening weighting (SSW) scheme that effectively differentiates between the sharp and smooth image components, contributing to a boosted variant of MS-GP<inline-formula><tex-math>$^{3}$</tex-math></inline-formula> (BMS-GP<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>) for further improvement in artifact suppression and detail restoration. Finally, embedding BMS-GP<inline-formula><tex-math>$^{3}$</tex-math></inline-formula> in half-quadratic splitting (HQS) iterations yields an advanced AMRI algorithm, dubbed BMS-GP<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>-HQS, which not only outperforms state-of-the-art (SOTA) methods but also provides robust theoretical guarantees, including ensured convergence and resilience to noise.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1880-1884"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929840","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

Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation 半监督语义分割中视觉转换器的标记级增强研究

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-04-21 DOI: 10.1109/LSP.2025.3562821

Dengke Zhang;Quan Tang;Fagui Liu;Haiqing Mei;C. L. Philip Chen

{"title":"Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation","authors":"Dengke Zhang;Quan Tang;Fagui Liu;Haiqing Mei;C. L. Philip Chen","doi":"10.1109/LSP.2025.3562821","DOIUrl":"https://doi.org/10.1109/LSP.2025.3562821","url":null,"abstract":"Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks, and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenSwap, a data augmentation technique designed explicitly for semi-supervised semantic segmentation with Vision Transformers. TokenSwap aligns well with the global attention mechanism by mixing images at the token level, enhancing the learning capability for contextual information among image patches and the utilization of unlabeled data. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1885-1889"},"PeriodicalIF":3.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929738","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