IEEE Signal Processing Letters最新文献_第6页

DNGG: Medical Image Lossless Encryption via Deep Network Guided Generative 基于深度网络引导生成的医学图像无损加密

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-18 DOI: 10.1109/LSP.2025.3552528

Lin Fan;Meng Li;Zhenting Hu;Yuan Hong;Dexing Kong

引用次数: 0

Optimizing the Order of Modes in Tensor Train Decomposition 张量列分解中模态阶数的优化

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-17 DOI: 10.1109/LSP.2025.3552005

Petr Tichavský;Ondřej Straka

引用次数: 0

Sensing Within Ultra-Short Duration: Extended Subspace Algorithms With Insufficient Snapshots 超短持续时间内的感知：快照不足的扩展子空间算法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-17 DOI: 10.1109/LSP.2025.3551943

Teng Ma;Yuxuan Feng;Yue Xiao;Xia Lei;Vladimir Poulkov

{"title":"Sensing Within Ultra-Short Duration: Extended Subspace Algorithms With Insufficient Snapshots","authors":"Teng Ma;Yuxuan Feng;Yue Xiao;Xia Lei;Vladimir Poulkov","doi":"10.1109/LSP.2025.3551943","DOIUrl":"https://doi.org/10.1109/LSP.2025.3551943","url":null,"abstract":"In pursuit of real-time sensing within ultra-short duration, conventional sensing algorithms are gradually failing to fulfill the stringent latency demands. Specifically, traditional subspace-based methods such as multiple signal classification (MUSIC) are hindered by their need for an extensive number of snapshots to accumulate the rank of the spatial covariance matrix (SCM), resulting in poor real-time performance. Moreover, advanced techniques like compressed sensing and machine learning are constrained by requirements for high signal sparsity or suffer from limited generality. To handle these challenges, this paper proposes an innovative extension of subspace theory tailored to insufficient-snapshot scenarios, leveraging the concept of spatio-temporal exchangeability. Based on the defined spatio-temporal correlation predicated on the space translation invariance characteristic of uniform linear arrays, we engineer a pseudo SCM that inherently possesses sufficient rank. This methodology not only resolves the rank-deficiency issue but also fully exploits the array aperture and significantly reduces the noise level. Simulation results are presented, substantiating the feasibility and enhanced performance of the proposed algorithms, marking a significant advancement over existing methodologies.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1490-1494"},"PeriodicalIF":3.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830431","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

A Two-Level Weighted Low-Complexity Adaptive Beamforming Method 一种两级加权低复杂度自适应波束形成方法

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-17 DOI: 10.1109/LSP.2025.3551957

Yuxi Du;Weijia Cui;Bin Ba;Yangyang Chen;Long Zhang

引用次数: 0

DCE-Net: A Dual-Frequency Domain Knowledge-Guided Framework for Image Dehazing via Detail and Content Enhancements DCE-Net：通过细节和内容增强的图像去雾的双频域知识引导框架

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-14 DOI: 10.1109/LSP.2025.3551201

Jianlei Liu;Yuting Pang;Shilong Wang

{"title":"DCE-Net: A Dual-Frequency Domain Knowledge-Guided Framework for Image Dehazing via Detail and Content Enhancements","authors":"Jianlei Liu;Yuting Pang;Shilong Wang","doi":"10.1109/LSP.2025.3551201","DOIUrl":"https://doi.org/10.1109/LSP.2025.3551201","url":null,"abstract":"Existing image dehazing methods are largely constrained to spatial domain processing, failing to fully leverage the rich knowledge embedded in the frequency domain of clear images. Additionally, the traditional convolutional operations in network architectures limit their mapping capabilities to some extent. To address these issues, a novel image dehazing network, termed the Detail and Content Enhancement Network (DCE-Net), is proposed. DCE-Net redefines dehazing task from a frequency-domain perspective, incorporating differential convolution and attention mechanisms to design the High-Frequency Detail Enhancement Module (HDEM) and the Low-Frequency Content Enhancement Module (LCEM). Furthermore, a Dual-Frequency Domain Knowledge-Guided Strategy (DDKS) is introduced during the training phase to exploit the abundant frequency-domain priors inherent in clear images. Experimental results demonstrate that the DCE-Net achieves outstanding performance on both synthetic benchmark datasets and real-world hazy scenes. DCE-Net not only significantly restores image clarity and contrast but also effectively preserves details and content features.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1356-1360"},"PeriodicalIF":3.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725107","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

Manifold Optimization for Distributed Phased-MIMO Radar Broad Beampattern Design 分布式相控mimo雷达宽波束图设计的流形优化

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-14 DOI: 10.1109/LSP.2025.3551653

Xueyin Geng;Jun Wang;Bin Yang;Zihao Li;Jinping Sun

引用次数: 0

Enhanced Attention Context Model for Learned Image Compression 学习图像压缩的增强注意上下文模型

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-14 DOI: 10.1109/LSP.2025.3551659

Zhengxin Chen;Xiaohai He;Chao Ren;Tingrong Zhang;Shuhua Xiong

{"title":"Enhanced Attention Context Model for Learned Image Compression","authors":"Zhengxin Chen;Xiaohai He;Chao Ren;Tingrong Zhang;Shuhua Xiong","doi":"10.1109/LSP.2025.3551659","DOIUrl":"https://doi.org/10.1109/LSP.2025.3551659","url":null,"abstract":"Recently, deep learning has witnessed encouraging advances in image compression. An accurate entropy model, which estimates the probability distribution of the latent representation and reduces the bits required for compressing an image, is one of the keys to the success of learned image compression methods. The latent representation presents potential correlations in local, non-local, and cross-channel contexts. However, most entropy models only consider partial correlations, leading to suboptimal entropy estimation. In this letter, we propose a novel enhanced attention context model (EACM) to make full use of various correlations between latent elements for accurate entropy estimation. The proposed EACM contains a local spatial attention block (LSAB), a local channel attention block (LCAB), a global spatial attention block (GSAB), and a global channel attention block (GCAB). LSAB, LCAB, GSAB, and GCAB are carefully designed to adaptively exploit local spatial, local channel, global spatial, and global channel correlations, respectively. The experimental results on benchmark datasets show that our image compression model with the proposed EACM outperforms several state-of-the-art methods quantitatively and qualitatively.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1301-1305"},"PeriodicalIF":3.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716527","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

XSNet: A Lightweight X-Ray Security Image Segmentation Model Combining State-Space Models and Convolutional Neural Networks 结合状态空间模型和卷积神经网络的轻量级x射线安全图像分割模型

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-14 DOI: 10.1109/LSP.2025.3550769

Weichao Jia;Wei Liu;Changsheng Zhang;Jian Fu;Qiong Liu

引用次数: 0

Algebraic Solution for Linear Array-Based 3D Localization Without Deployment Limitations 无部署限制的线性阵列三维定位的代数解

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-14 DOI: 10.1109/LSP.2025.3551200

Chengyu Li;Beichuan Tang;Yanbing Yang;Liangyin Chen;Yimao Sun

引用次数: 0

A Distributed Penalty-Like Function Approach for the Nonconvex Constrained Optimization Problem 非凸约束优化问题的一种分布式类罚函数方法