IEEE Signal Processing Letters最新文献_第7页

Enhanced Swin Transformer and Edge Spatial Attention for Remote Sensing Image Semantic Segmentation 基于Swin变压器和边缘空间关注的遥感图像语义分割

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-12 DOI: 10.1109/LSP.2025.3550858

Fuxiang Liu;Zhiqiang Hu;Lei Li;Hanlu Li;Xinxin Liu

{"title":"Enhanced Swin Transformer and Edge Spatial Attention for Remote Sensing Image Semantic Segmentation","authors":"Fuxiang Liu;Zhiqiang Hu;Lei Li;Hanlu Li;Xinxin Liu","doi":"10.1109/LSP.2025.3550858","DOIUrl":"https://doi.org/10.1109/LSP.2025.3550858","url":null,"abstract":"Combining convolutional neural networks (CNNs) and transformers is a crucial direction in remote sensing image semantic segmentation. However, due to differences in the spatial information focus and feature extraction methods, existing feature transfer and fusion strategies do not effectively integrate the advantages of both approaches. To address these issues, we propose a CNN-transformer hybrid network for precise remote sensing image semantic segmentation. We propose a novel Swin Transformer block to optimize feature extraction and enable the model to handle remote sensing images of arbitrary sizes. Additionally, we design an Edge Spatial Attention module to focus attention on local edge structures, effectively integrating global features and local details. This facilitates efficient information flow between the Transformer encoder and CNN decoder. Finally, a multi-scale convolutional decoder is employed to fully leverage both global information from the Transformer and local features from the CNN, leading to accurate segmentation results. Our network achieved state-of-the-art performance on the Vaihingen and Potsdam datasets, reaching mIoU and F1 scores of 67.37% and 79.82%, as well as 72.39% and 83.68%, respectively.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1296-1300"},"PeriodicalIF":3.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716509","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

Bias-Compensated Normalized Iterative Wiener Filter Algorithm With Noisy Input 带噪声输入的偏置补偿归一化迭代维纳滤波算法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-11 DOI: 10.1109/LSP.2025.3550772

Hai Yuan;Lu Lu;Guangya Zhu;Badong Chen

引用次数: 0

MMA: Video Reconstruction for Spike Camera Based on Multiscale Temporal Modeling and Fine-Grained Attention 基于多尺度时间建模和细粒度注意力的长钉摄像机视频重构

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-11 DOI: 10.1109/LSP.2025.3550278

Dilmurat Alim;Chen Yang;Laiyun Qing;Guorong Li;Qingming Huang

{"title":"MMA: Video Reconstruction for Spike Camera Based on Multiscale Temporal Modeling and Fine-Grained Attention","authors":"Dilmurat Alim;Chen Yang;Laiyun Qing;Guorong Li;Qingming Huang","doi":"10.1109/LSP.2025.3550278","DOIUrl":"https://doi.org/10.1109/LSP.2025.3550278","url":null,"abstract":"This paper presents a Multiscale Temporal Correlation Learning with the Mamba-Fused Attention Model (MMA), an efficient and effective method for reconstructing a video clip from a spike stream. Spike cameras offer unique advantages for capturing rapid scene changes with high temporal resolution. A spike stream contains sufficient information for multiple image reconstructions. However, existing methods generate only a single image at a time for a given spike stream, which results in excessive redundant computations between consecutive frames when aiming at restoring a video clip, thereby increasing computational costs significantly. The proposed MMA addresses such challenges by constructing a spike-to-video model, directly producing an image sequence at a time. Specifically, we propose a U-shaped Multiscale Temporal Correlation Learning (MTCL) to fuse the features at different temporal resolutions for clear video reconstruction. At each scale, we introduce a Fine-Grained Attention (FGA) module for fine-spatial context modeling within a patch and a Mamba module for integrating features across patches. Adopting a lightweight U-shaped structure and fine-grained feature extraction at each level, our method reconstructs high-quality image sequences quickly. The experimental results show that the proposed MMA surpasses current state-of-the-art methods in image quality, computation cost, and model size.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1291-1295"},"PeriodicalIF":3.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716508","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

Vision-Inspired Boundary Perception Network for Lightweight Camouflaged Object Detection 轻量级伪装目标检测的视觉启发边界感知网络

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-11 DOI: 10.1109/LSP.2025.3549698

Chunyuan Chen;Weiyun Liang;Donglin Wang;Bin Wang;Jing Xu

{"title":"Vision-Inspired Boundary Perception Network for Lightweight Camouflaged Object Detection","authors":"Chunyuan Chen;Weiyun Liang;Donglin Wang;Bin Wang;Jing Xu","doi":"10.1109/LSP.2025.3549698","DOIUrl":"https://doi.org/10.1109/LSP.2025.3549698","url":null,"abstract":"Lightweight camouflaged object detection (COD) has garnered increasing attention due to its wide range of real-world applications and its efficiency on mobile devices. Existing lightweight COD methods typically attempt to utilize multi-scale fusion, frequency cues, and texture information to enhance the representation ability of lightweight backbone features. However, they still fall short in detecting precise and continuous object boundaries. To address this issue, we observe that two types of cells in the human visual system make great contributions to boundary perception. Motivated by this, we propose a boundary perception module (BPM) to enhance features with the awareness of fine-grained boundary, by mimicking the boundary perception process of aforementioned cells. In addition, we propose a bidirectional semantic enhancement module (BSEM) to effectively decode multi-level features in a lightweight manner. With BPM and BSEM, our proposed vision-inspired boundary perception network (BPNet) achieves superior performance against state-of-the-art methods and surpasses lightweight COD models by a large margin with the least parameters (3.64 M) and fastest speed (168FPS for the input size of 384 × 384).","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1176-1180"},"PeriodicalIF":3.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667270","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

Dual-Stream Image Sharing Chain Detection via Dynamic Information Compensation 基于动态信息补偿的双流图像共享链检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-11 DOI: 10.1109/LSP.2025.3550282

Xinyi Su;Yuanman Li;Yulong Zheng;Xia Li

引用次数: 0

Optimal Sample Acquisition for Optimally Weighted PCA From Heterogeneous Quality Sources 基于异构质量源的最优加权PCA的最优样本采集

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-11 DOI: 10.1109/LSP.2025.3550280

David Hong;Laura Balzano

引用次数: 0

Momentum-Based Iterative Hard Thresholding Algorithm for Sparse Signal Recovery 基于动量的稀疏信号恢复迭代硬阈值算法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-11 DOI: 10.1109/LSP.2025.3550768

Wen Jin;Lie-Jun Xie

引用次数: 0

Power Transformed Density Ridge Estimation 功率转换密度脊估计

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-10 DOI: 10.1109/LSP.2025.3549699

Hengchao Chen;Zheng Zhai

{"title":"Power Transformed Density Ridge Estimation","authors":"Hengchao Chen;Zheng Zhai","doi":"10.1109/LSP.2025.3549699","DOIUrl":"https://doi.org/10.1109/LSP.2025.3549699","url":null,"abstract":"This paper proposes to control ridge estimation with nonlinear transformations. We establish an inclusion relationship between ridges with/without transformations: <inline-formula><tex-math>${mathcal R}(fcirc p)subseteq {mathcal R}(p)$</tex-math></inline-formula>, where <inline-formula><tex-math>${mathcal R}(p)$</tex-math></inline-formula> is the <inline-formula><tex-math>$d$</tex-math></inline-formula>-dimensional ridge of <inline-formula><tex-math>$p:mathbb {R}^{D}to mathbb {R}$</tex-math></inline-formula> and <inline-formula><tex-math>$f$</tex-math></inline-formula> is a strictly increasing and concave map defined on range<inline-formula><tex-math>$(p)$</tex-math></inline-formula>. This finding reveals the benefit of applying an increasing and concave transformation before ridge estimation. As <inline-formula><tex-math>$f^{q}(y)=y^{q}/q, qleq 1$</tex-math></inline-formula> are increasing and concave on <inline-formula><tex-math>$mathbb {R}_+$</tex-math></inline-formula>, we use these power transformations to positive density functions, and then perform ridge estimation. The algorithm, named power-transformed subspace-constrained mean-shift (PSCMS), outperforms its competitors in numerical experiments.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1286-1290"},"PeriodicalIF":3.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716488","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

Efficient CNN Prediction With Smoothness Factor for Reversible Data Hiding 基于平滑因子的CNN可逆数据隐藏预测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-10 DOI: 10.1109/LSP.2025.3549704

Minchun Lin;Shijun Xiang

引用次数: 0

Effective Coherent Integration of Agile Echo Signal via Improved Sparse Adaptive Matching Pursuit 基于改进稀疏自适应匹配跟踪的敏捷回波信号有效相干集成

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-10 DOI: 10.1109/LSP.2025.3550046

Ping Lang;Xiongjun Fu;Jian Dong;Junjun Yin;Jian Yang

引用次数: 0